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toggleContents.style.height = \"0px\";\n\n tocShowing = false;\n\n }\n\n };\n\n\n\n \/\/ Get rid of any expanded toggle if the user scrolls\n\n window.document.addEventListener(\n\n \"scroll\",\n\n throttle(() => {\n\n closeToggle();\n\n }, 50)\n\n );\n\n\n\n \/\/ Handle positioning of the toggle\n\n window.addEventListener(\n\n \"resize\",\n\n throttle(() => {\n\n elRect = undefined;\n\n positionToggle();\n\n }, 50)\n\n );\n\n\n\n window.addEventListener(\"quarto-hrChanged\", () => {\n\n elRect = undefined;\n\n });\n\n\n\n \/\/ Process the click\n\n clickEl.onclick = () => {\n\n if (!tocShowing) {\n\n toggleContainer.classList.add(\"expanded\");\n\n toggleContents.style.height = null;\n\n tocShowing = true;\n\n } else {\n\n closeToggle();\n\n }\n\n };\n\n });\n\n };\n\n\n\n \/\/ Converts a sidebar from a menu back to a sidebar\n\n const convertToSidebar = () => {\n\n for (const child of el.children) {\n\n child.style.opacity = 1;\n\n child.style.overflow = null;\n\n }\n\n\n\n const placeholderEl = window.document.getElementById(\n\n placeholderDescriptor.id\n\n );\n\n if (placeholderEl) {\n\n placeholderEl.remove();\n\n }\n\n\n\n el.classList.remove(\"rollup\");\n\n };\n\n\n\n if (isReaderMode()) {\n\n convertToMenu();\n\n isVisible = false;\n\n } else {\n\n \/\/ Find the top and bottom o the element that is being managed\n\n const elTop = el.offsetTop;\n\n const elBottom =\n\n elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;\n\n\n\n if (!isVisible) {\n\n \/\/ If the element is current not visible reveal if there are\n\n \/\/ no conflicts with overlay regions\n\n if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {\n\n convertToSidebar();\n\n isVisible = true;\n\n }\n\n } else {\n\n \/\/ If the element is visible, hide it if it conflicts with overlay regions\n\n \/\/ and insert a placeholder toggle (or if we're in reader mode)\n\n if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {\n\n convertToMenu();\n\n isVisible = false;\n\n }\n\n }\n\n }\n\n }\n\n };\n\n };\n\n\n\n const tabEls = document.querySelectorAll('a[data-bs-toggle=\"tab\"]');\n\n for (const tabEl of tabEls) {\n\n const id = tabEl.getAttribute(\"data-bs-target\");\n\n if (id) {\n\n const columnEl = document.querySelector(\n\n `${id} .column-margin, .tabset-margin-content`\n\n );\n\n if (columnEl)\n\n tabEl.addEventListener(\"shown.bs.tab\", function (event) {\n\n const el = event.srcElement;\n\n if (el) {\n\n const visibleCls = `${el.id}-margin-content`;\n\n \/\/ walk up until we find a parent tabset\n\n let panelTabsetEl = el.parentElement;\n\n while (panelTabsetEl) {\n\n if (panelTabsetEl.classList.contains(\"panel-tabset\")) {\n\n break;\n\n }\n\n panelTabsetEl = panelTabsetEl.parentElement;\n\n }\n\n\n\n if (panelTabsetEl) {\n\n const prevSib = panelTabsetEl.previousElementSibling;\n\n if (\n\n prevSib &&\n\n prevSib.classList.contains(\"tabset-margin-container\")\n\n ) {\n\n const childNodes = prevSib.querySelectorAll(\n\n \".tabset-margin-content\"\n\n );\n\n for (const childEl of childNodes) {\n\n if (childEl.classList.contains(visibleCls)) {\n\n childEl.classList.remove(\"collapse\");\n\n } else {\n\n childEl.classList.add(\"collapse\");\n\n }\n\n }\n\n }\n\n }\n\n }\n\n\n\n layoutMarginEls();\n\n });\n\n }\n\n }\n\n\n\n \/\/ Manage the visibility of the toc and the sidebar\n\n const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {\n\n id: \"quarto-toc-toggle\",\n\n titleSelector: \"#toc-title\",\n\n dismissOnClick: true,\n\n });\n\n const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {\n\n id: \"quarto-sidebarnav-toggle\",\n\n titleSelector: \".title\",\n\n dismissOnClick: false,\n\n });\n\n let tocLeftScrollVisibility;\n\n if (leftTocEl) {\n\n tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {\n\n id: \"quarto-lefttoc-toggle\",\n\n titleSelector: \"#toc-title\",\n\n dismissOnClick: true,\n\n });\n\n }\n\n\n\n \/\/ Find the first element that uses formatting in special columns\n\n const conflictingEls = window.document.body.querySelectorAll(\n\n '[class^=\"column-\"], [class*=\" column-\"], aside, [class*=\"margin-caption\"], [class*=\" margin-caption\"], [class*=\"margin-ref\"], [class*=\" margin-ref\"]'\n\n );\n\n\n\n \/\/ Filter all the possibly conflicting elements into ones\n\n \/\/ the do conflict on the left or ride side\n\n const arrConflictingEls = Array.from(conflictingEls);\n\n const leftSideConflictEls = arrConflictingEls.filter((el) => {\n\n if (el.tagName === \"ASIDE\") {\n\n return false;\n\n }\n\n return Array.from(el.classList).find((className) => {\n\n return (\n\n className !== \"column-body\" &&\n\n className.startsWith(\"column-\") &&\n\n !className.endsWith(\"right\") &&\n\n !className.endsWith(\"container\") &&\n\n className !== \"column-margin\"\n\n );\n\n });\n\n });\n\n const rightSideConflictEls = arrConflictingEls.filter((el) => {\n\n if (el.tagName === \"ASIDE\") {\n\n return true;\n\n }\n\n\n\n const hasMarginCaption = Array.from(el.classList).find((className) => {\n\n return className == \"margin-caption\";\n\n });\n\n if (hasMarginCaption) {\n\n return true;\n\n }\n\n\n\n return Array.from(el.classList).find((className) => {\n\n return (\n\n className !== \"column-body\" &&\n\n !className.endsWith(\"container\") &&\n\n className.startsWith(\"column-\") &&\n\n !className.endsWith(\"left\")\n\n );\n\n });\n\n });\n\n\n\n const kOverlapPaddingSize = 10;\n\n function toRegions(els) {\n\n return els.map((el) => {\n\n const boundRect = el.getBoundingClientRect();\n\n const top =\n\n boundRect.top +\n\n document.documentElement.scrollTop -\n\n kOverlapPaddingSize;\n\n return {\n\n top,\n\n bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,\n\n };\n\n });\n\n }\n\n\n\n let hasObserved = false;\n\n const visibleItemObserver = (els) => {\n\n let visibleElements = [...els];\n\n const intersectionObserver = new IntersectionObserver(\n\n (entries, _observer) => {\n\n entries.forEach((entry) => {\n\n if (entry.isIntersecting) {\n\n if (visibleElements.indexOf(entry.target) === -1) {\n\n visibleElements.push(entry.target);\n\n }\n\n } else {\n\n visibleElements = visibleElements.filter((visibleEntry) => {\n\n return visibleEntry !== entry;\n\n });\n\n }\n\n });\n\n\n\n if (!hasObserved) {\n\n hideOverlappedSidebars();\n\n }\n\n hasObserved = true;\n\n },\n\n {}\n\n );\n\n els.forEach((el) => {\n\n intersectionObserver.observe(el);\n\n });\n\n\n\n return {\n\n getVisibleEntries: () => {\n\n return visibleElements;\n\n },\n\n };\n\n };\n\n\n\n const rightElementObserver = visibleItemObserver(rightSideConflictEls);\n\n const leftElementObserver = visibleItemObserver(leftSideConflictEls);\n\n\n\n const hideOverlappedSidebars = () => {\n\n marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));\n\n sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));\n\n if (tocLeftScrollVisibility) {\n\n tocLeftScrollVisibility(\n\n toRegions(leftElementObserver.getVisibleEntries())\n\n );\n\n }\n\n };\n\n\n\n window.quartoToggleReader = () => {\n\n \/\/ Applies a slow class (or removes it)\n\n \/\/ to update the transition speed\n\n const slowTransition = (slow) => {\n\n const manageTransition = (id, slow) => {\n\n const el = document.getElementById(id);\n\n if (el) {\n\n if (slow) {\n\n el.classList.add(\"slow\");\n\n } else {\n\n el.classList.remove(\"slow\");\n\n }\n\n }\n\n };\n\n\n\n manageTransition(\"TOC\", slow);\n\n manageTransition(\"quarto-sidebar\", slow);\n\n };\n\n const readerMode = !isReaderMode();\n\n setReaderModeValue(readerMode);\n\n\n\n \/\/ If we're entering reader mode, slow the transition\n\n if (readerMode) {\n\n slowTransition(readerMode);\n\n }\n\n highlightReaderToggle(readerMode);\n\n hideOverlappedSidebars();\n\n\n\n \/\/ If we're exiting reader mode, restore the non-slow transition\n\n if (!readerMode) {\n\n slowTransition(!readerMode);\n\n }\n\n };\n\n\n\n const highlightReaderToggle = (readerMode) => {\n\n const els = document.querySelectorAll(\".quarto-reader-toggle\");\n\n if (els) {\n\n els.forEach((el) => {\n\n if (readerMode) {\n\n el.classList.add(\"reader\");\n\n } else {\n\n el.classList.remove(\"reader\");\n\n }\n\n });\n\n }\n\n };\n\n\n\n const setReaderModeValue = (val) => {\n\n if (window.location.protocol !== \"file:\") {\n\n window.localStorage.setItem(\"quarto-reader-mode\", val);\n\n } else {\n\n localReaderMode = val;\n\n }\n\n };\n\n\n\n const isReaderMode = () => {\n\n if (window.location.protocol !== \"file:\") {\n\n return window.localStorage.getItem(\"quarto-reader-mode\") === \"true\";\n\n } else {\n\n return localReaderMode;\n\n }\n\n };\n\n let localReaderMode = null;\n\n\n\n const tocOpenDepthStr = tocEl?.getAttribute(\"data-toc-expanded\");\n\n const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;\n\n\n\n \/\/ Walk the TOC and collapse\/expand nodes\n\n \/\/ Nodes are expanded if:\n\n \/\/ - they are top level\n\n \/\/ - they have children that are 'active' links\n\n \/\/ - they are directly below an link that is 'active'\n\n const walk = (el, depth) => {\n\n \/\/ Tick depth when we enter a UL\n\n if (el.tagName === \"UL\") {\n\n depth = depth + 1;\n\n }\n\n\n\n \/\/ It this is active link\n\n let isActiveNode = false;\n\n if (el.tagName === \"A\" && el.classList.contains(\"active\")) {\n\n isActiveNode = true;\n\n }\n\n\n\n \/\/ See if there is an active child to this element\n\n let hasActiveChild = false;\n\n for (child of el.children) {\n\n hasActiveChild = walk(child, depth) || hasActiveChild;\n\n }\n\n\n\n \/\/ Process the collapse state if this is an UL\n\n if (el.tagName === \"UL\") {\n\n if (tocOpenDepth === -1 && depth > 1) {\n\n el.classList.add(\"collapse\");\n\n } else if (\n\n depth <= tocOpenDepth ||\n\n hasActiveChild ||\n\n prevSiblingIsActiveLink(el)\n\n ) {\n\n el.classList.remove(\"collapse\");\n\n } else {\n\n el.classList.add(\"collapse\");\n\n }\n\n\n\n \/\/ untick depth when we leave a UL\n\n depth = depth - 1;\n\n }\n\n return hasActiveChild || isActiveNode;\n\n };\n\n\n\n \/\/ walk the TOC and expand \/ collapse any items that should be shown\n\n\n\n if (tocEl) {\n\n walk(tocEl, 0);\n\n updateActiveLink();\n\n }\n\n\n\n \/\/ Throttle the scroll event and walk peridiocally\n\n window.document.addEventListener(\n\n \"scroll\",\n\n throttle(() => {\n\n if (tocEl) {\n\n updateActiveLink();\n\n walk(tocEl, 0);\n\n }\n\n if (!isReaderMode()) {\n\n hideOverlappedSidebars();\n\n }\n\n }, 5)\n\n );\n\n window.addEventListener(\n\n \"resize\",\n\n throttle(() => {\n\n if (!isReaderMode()) {\n\n hideOverlappedSidebars();\n\n }\n\n }, 10)\n\n );\n\n hideOverlappedSidebars();\n\n highlightReaderToggle(isReaderMode());\n\n});\n\n\n\n\/\/ grouped tabsets\n\nwindow.addEventListener(\"pageshow\", (_event) => {\n\n function getTabSettings() {\n\n const data = localStorage.getItem(\"quarto-persistent-tabsets-data\");\n\n if (!data) {\n\n localStorage.setItem(\"quarto-persistent-tabsets-data\", \"{}\");\n\n return {};\n\n }\n\n if (data) {\n\n return JSON.parse(data);\n\n }\n\n }\n\n\n\n function setTabSettings(data) {\n\n localStorage.setItem(\n\n \"quarto-persistent-tabsets-data\",\n\n JSON.stringify(data)\n\n );\n\n }\n\n\n\n function setTabState(groupName, groupValue) {\n\n const data = getTabSettings();\n\n data[groupName] = groupValue;\n\n setTabSettings(data);\n\n }\n\n\n\n function toggleTab(tab, active) {\n\n const tabPanelId = tab.getAttribute(\"aria-controls\");\n\n const tabPanel = document.getElementById(tabPanelId);\n\n if (active) {\n\n tab.classList.add(\"active\");\n\n tabPanel.classList.add(\"active\");\n\n } else {\n\n tab.classList.remove(\"active\");\n\n tabPanel.classList.remove(\"active\");\n\n }\n\n }\n\n\n\n function toggleAll(selectedGroup, selectorsToSync) {\n\n for 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Object.keys(f).sort((function(t,e){return f[t]-f[e]}))}const si={name:\"flip\",enabled:!0,phase:\"main\",fn:function(t){var e=t.state,i=t.options,n=t.name;if(!e.modifiersData[n]._skip){for(var s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0===r||r,l=i.fallbackPlacements,c=i.padding,h=i.boundary,d=i.rootBoundary,u=i.altBoundary,f=i.flipVariations,p=void 0===f||f,m=i.allowedAutoPlacements,g=e.options.placement,_=be(g),b=l||(_!==g&&p?function(t){if(be(t)===Kt)return[];var e=Ve(t);return[Qe(t),e,Qe(e)]}(g):[Ve(g)]),v=[g].concat(b).reduce((function(t,i){return t.concat(be(i)===Kt?ni(e,{placement:i,boundary:h,rootBoundary:d,padding:c,flipVariations:p,allowedAutoPlacements:m}):i)}),[]),y=e.rects.reference,w=e.rects.popper,A=new Map,E=!0,T=v[0],C=0;C<v.length;C++){var O=v[C],x=be(O),k=Fe(O)===Xt,L=[zt,Rt].indexOf(x)>=0,S=L?\"width\":\"height\",D=ii(e,{placement:O,boundary:h,rootBoundary:d,altBoundary:u,padding:c}),$=L?k?qt:Vt:k?Rt:zt;y[S]>w[S]&&($=Ve($));var 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e=t.state,i=t.name,n=e.rects.reference,s=e.rects.popper,o=e.modifiersData.preventOverflow,r=ii(e,{elementContext:\"reference\"}),a=ii(e,{altBoundary:!0}),l=oi(r,n),c=oi(a,s,o),h=ri(l),d=ri(c);e.modifiersData[i]={referenceClippingOffsets:l,popperEscapeOffsets:c,isReferenceHidden:h,hasPopperEscaped:d},e.attributes.popper=Object.assign({},e.attributes.popper,{\"data-popper-reference-hidden\":h,\"data-popper-escaped\":d})}},li={name:\"offset\",enabled:!0,phase:\"main\",requires:[\"popperOffsets\"],fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=ee.reduce((function(t,i){return t[i]=function(t,e,i){var n=be(t),s=[Vt,zt].indexOf(n)>=0?-1:1,o=\"function\"==typeof i?i(Object.assign({},e,{placement:t})):i,r=o[0],a=o[1];return 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vi=Object.freeze(Object.defineProperty({__proto__:null,afterMain:ae,afterRead:se,afterWrite:he,applyStyles:_e,arrow:je,auto:Kt,basePlacements:Qt,beforeMain:oe,beforeRead:ie,beforeWrite:le,bottom:Rt,clippingParents:Ut,computeStyles:Be,createPopper:bi,createPopperBase:gi,createPopperLite:_i,detectOverflow:ii,end:Yt,eventListeners:Re,flip:si,hide:ai,left:Vt,main:re,modifierPhases:de,offset:li,placements:ee,popper:Jt,popperGenerator:mi,popperOffsets:ci,preventOverflow:hi,read:ne,reference:Zt,right:qt,start:Xt,top:zt,variationPlacements:te,viewport:Gt,write:ce},Symbol.toStringTag,{value:\"Module\"})),yi=\"dropdown\",wi=\".bs.dropdown\",Ai=\".data-api\",Ei=\"ArrowUp\",Ti=\"ArrowDown\",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ai}`,Si=`keydown${wi}${Ai}`,Di=`keyup${wi}${Ai}`,$i=\"show\",Ii='[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=\".dropdown-menu\",Mi=p()?\"top-end\":\"top-start\",ji=p()?\"top-start\":\"top-end\",Fi=p()?\"bottom-end\":\"bottom-start\",Hi=p()?\"bottom-start\":\"bottom-end\",Wi=p()?\"left-start\":\"right-start\",Bi=p()?\"right-start\":\"left-start\",zi={autoClose:!0,boundary:\"clippingParents\",display:\"dynamic\",offset:[0,2],popperConfig:null,reference:\"toggle\"},Ri={autoClose:\"(boolean|string)\",boundary:\"(string|element)\",display:\"string\",offset:\"(array|string|function)\",popperConfig:\"(null|object|function)\",reference:\"(string|element|object)\"};class qi extends W{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=z.next(this._element,Pi)[0]||z.prev(this._element,Pi)[0]||z.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!N.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),\"ontouchstart\"in document.documentElement&&!this._parent.closest(\".navbar-nav\"))for(const t of[].concat(...document.body.children))N.on(t,\"mouseover\",h);this._element.focus(),this._element.setAttribute(\"aria-expanded\",!0),this._menu.classList.add($i),this._element.classList.add($i),N.trigger(this._element,ki,t)}}hide(){if(l(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!N.trigger(this._element,Ci,t).defaultPrevented){if(\"ontouchstart\"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,\"mouseover\",h);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute(\"aria-expanded\",\"false\"),F.removeDataAttribute(this._menu,\"popper\"),N.trigger(this._element,Oi,t)}}_getConfig(t){if(\"object\"==typeof(t=super._getConfig(t)).reference&&!o(t.reference)&&\"function\"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);return t}_createPopper(){if(void 0===vi)throw new TypeError(\"Bootstrap's dropdowns require Popper (https:\/\/popper.js.org)\");let t=this._element;\"parent\"===this._config.reference?t=this._parent:o(this._config.reference)?t=r(this._config.reference):\"object\"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=bi(t,this._menu,e)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains(\"dropend\"))return Wi;if(t.classList.contains(\"dropstart\"))return Bi;if(t.classList.contains(\"dropup-center\"))return\"top\";if(t.classList.contains(\"dropdown-center\"))return\"bottom\";const e=\"end\"===getComputedStyle(this._menu).getPropertyValue(\"--bs-position\").trim();return t.classList.contains(\"dropup\")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(\".navbar\")}_getOffset(){const{offset:t}=this._config;return\"string\"==typeof t?t.split(\",\").map((t=>Number.parseInt(t,10))):\"function\"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:\"preventOverflow\",options:{boundary:this._config.boundary}},{name:\"offset\",options:{offset:this._getOffset()}}]};return(this._inNavbar||\"static\"===this._config.display)&&(F.setDataAttribute(this._menu,\"popper\",\"static\"),t.modifiers=[{name:\"applyStyles\",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(\".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)\",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if(\"string\"==typeof t){if(void 0===e[t])throw new TypeError(`No method named \"${t}\"`);e[t]()}}))}static clearMenus(t){if(2===t.button||\"keyup\"===t.type&&\"Tab\"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||\"inside\"===e._config.autoClose&&!s||\"outside\"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&(\"keyup\"===t.type&&\"Tab\"===t.key||\/input|select|option|textarea|form\/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};\"click\"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=\/input|textarea\/i.test(t.target.tagName),i=\"Escape\"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi=\"backdrop\",Ki=\"show\",Qi=`mousedown.bs.${Vi}`,Xi={className:\"modal-backdrop\",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:\"body\"},Yi={className:\"string\",clickCallback:\"(function|null)\",isAnimated:\"boolean\",isVisible:\"boolean\",rootElement:\"(element|string)\"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement(\"div\");t.className=this._config.className,this._config.isAnimated&&t.classList.add(\"fade\"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=\".bs.focustrap\",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn=\"backward\",en={autofocus:!0,trapElement:null},nn={autofocus:\"boolean\",trapElement:\"element\"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return\"focustrap\"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){\"Tab\"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:\"forward\")}}const on=\".fixed-top, .fixed-bottom, .is-fixed, .sticky-top\",rn=\".sticky-top\",an=\"padding-right\",ln=\"margin-right\";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,\"overflow\"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,\"overflow\"),this._element.style.overflow=\"hidden\"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=\".bs.modal\",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn=\"modal-open\",An=\"show\",En=\"modal-static\",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:\"(boolean|string)\",focus:\"boolean\",keyboard:\"boolean\"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(\".modal-dialog\",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return\"modal\"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display=\"block\",this._element.removeAttribute(\"aria-hidden\"),this._element.setAttribute(\"aria-modal\",!0),this._element.setAttribute(\"role\",\"dialog\"),this._element.scrollTop=0;const e=z.findOne(\".modal-body\",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{\"Escape\"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&(\"static\"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display=\"none\",this._element.setAttribute(\"aria-hidden\",!0),this._element.removeAttribute(\"aria-modal\"),this._element.removeAttribute(\"role\"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains(\"fade\")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;\"hidden\"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY=\"hidden\"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?\"paddingLeft\":\"paddingRight\";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?\"paddingRight\":\"paddingLeft\";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft=\"\",this._element.style.paddingRight=\"\"}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if(\"string\"==typeof t){if(void 0===i[t])throw new TypeError(`No method named \"${t}\"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle=\"modal\"]',(function(t){const e=z.getElementFromSelector(this);[\"A\",\"AREA\"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(\".modal.show\");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=\".bs.offcanvas\",kn=\".data-api\",Ln=`load${xn}${kn}`,Sn=\"show\",Dn=\"showing\",$n=\"hiding\",In=\".offcanvas.show\",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:\"(boolean|string)\",keyboard:\"boolean\",scroll:\"boolean\"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return\"offcanvas\"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute(\"aria-modal\",!0),this._element.setAttribute(\"role\",\"dialog\"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute(\"aria-modal\"),this._element.removeAttribute(\"role\"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:\"offcanvas-backdrop\",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{\"static\"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{\"Escape\"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if(\"string\"==typeof t){if(void 0===e[t]||t.startsWith(\"_\")||\"constructor\"===t)throw new TypeError(`No method named \"${t}\"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle=\"offcanvas\"]',(function(t){const e=z.getElementFromSelector(this);if([\"A\",\"AREA\"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find(\"[aria-modal][class*=show][class*=offcanvas-]\"))\"fixed\"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={\"*\":[\"class\",\"dir\",\"id\",\"lang\",\"role\",\/^aria-[\\w-]*$\/i],a:[\"target\",\"href\",\"title\",\"rel\"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:[\"src\",\"srcset\",\"alt\",\"title\",\"width\",\"height\"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set([\"background\",\"cite\",\"href\",\"itemtype\",\"longdesc\",\"poster\",\"src\",\"xlink:href\"]),Qn=\/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:\/?#]*(?:[\/?#]|$))\/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:\"\",html:!1,sanitize:!0,sanitizeFn:null,template:\"<div><\/div>\"},Un={allowList:\"object\",content:\"object\",extraClass:\"(string|function)\",html:\"boolean\",sanitize:\"boolean\",sanitizeFn:\"(null|function)\",template:\"string\"},Gn={entry:\"(string|element|function|null)\",selector:\"(string|element)\"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return\"TemplateFactory\"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement(\"div\");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(\" \")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&\"function\"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,\"text\/html\"),s=[].concat(...n.body.querySelectorAll(\"*\"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e[\"*\"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML=\"\",void e.append(t);e.textContent=t.textContent}}const Zn=new Set([\"sanitize\",\"allowList\",\"sanitizeFn\"]),ts=\"fade\",es=\"show\",is=\".modal\",ns=\"hide.bs.modal\",ss=\"hover\",os=\"focus\",rs={AUTO:\"auto\",TOP:\"top\",RIGHT:p()?\"left\":\"right\",BOTTOM:\"bottom\",LEFT:p()?\"right\":\"left\"},as={allowList:Vn,animation:!0,boundary:\"clippingParents\",container:!1,customClass:\"\",delay:0,fallbackPlacements:[\"top\",\"right\",\"bottom\",\"left\"],html:!1,offset:[0,6],placement:\"top\",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class=\"tooltip\" role=\"tooltip\"><div class=\"tooltip-arrow\"><\/div><div class=\"tooltip-inner\"><\/div><\/div>',title:\"\",trigger:\"hover focus\"},ls={allowList:\"object\",animation:\"boolean\",boundary:\"(string|element)\",container:\"(string|element|boolean)\",customClass:\"(string|function)\",delay:\"(number|object)\",fallbackPlacements:\"array\",html:\"boolean\",offset:\"(array|string|function)\",placement:\"(string|function)\",popperConfig:\"(null|object|function)\",sanitize:\"boolean\",sanitizeFn:\"(null|function)\",selector:\"(string|boolean)\",template:\"string\",title:\"(string|element|function)\",trigger:\"string\"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError(\"Bootstrap's tooltips require Popper (https:\/\/popper.js.org)\");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return\"tooltip\"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute(\"data-bs-original-title\")&&this._element.setAttribute(\"title\",this._element.getAttribute(\"data-bs-original-title\")),this._disposePopper(),super.dispose()}show(){if(\"none\"===this._element.style.display)throw new Error(\"Please use show on visible elements\");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName(\"show\")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute(\"aria-describedby\",i.getAttribute(\"id\"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName(\"inserted\"))),this._popper=this._createPopper(i),i.classList.add(es),\"ontouchstart\"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,\"mouseover\",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName(\"shown\")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName(\"hide\")).defaultPrevented){if(this._getTipElement().classList.remove(es),\"ontouchstart\"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,\"mouseover\",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute(\"aria-describedby\"),N.trigger(this._element,this.constructor.eventName(\"hidden\")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return 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id=\"quarto-bootstrap\" rel=\"stylesheet\"\/>\n<script>$(document).ready(function(){\n\n if (typeof $('[data-toggle=\"tooltip\"]').tooltip === 'function') {\n\n $('[data-toggle=\"tooltip\"]').tooltip();\n\n }\n\n if ($('[data-toggle=\"popover\"]').popover === 'function') {\n\n $('[data-toggle=\"popover\"]').popover();\n\n }\n\n});\n\n<\/script>\n\n<\/head>\n<body class=\"fullcontent\">\n<div class=\"page-layout-custom\">\n<header class=\"quarto-title-block default\" id=\"title-block-header\">\n<div class=\"quarto-title\">\n\nWhy a team that has good (but not great) ideas 75% of the time may see a win rate of just 25%, and why focusing on MDE\/statistical power alone may not always be the best<\/p>\n<\/div>\n<div class=\"quarto-title-meta\">\n<\/div>\n<\/header>\n<img decoding=\"async\" class=\"img-fluid\" 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\"\/><\/p>\nClassical statistical guarantees in A\/B testing (Type I and II error rates) are great at quantifying the reproducibility of a single experiment. As a reminder:<\/p>\n<ul>\n<li>Type I error rate, <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b1<\/mi><annotation encoding=\"application\/x-tex\">\\alpha<\/annotation><\/semantics><\/math>, guarantees that when the null hypothesis is true (typically: no difference between variants), you will only observe statistically significant results (i.e., observed data says there\u2019s a difference) <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b1<\/mi><annotation encoding=\"application\/x-tex\">\\alpha<\/annotation><\/semantics><\/math> fraction of the time. It\u2019s the false positive rate for an experiment with no effect.<\/p><\/li>\n<li>Type II error rate, <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mn>1<\/mn><mo>\u2212<\/mo><mi>\u03b2<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">1-\\beta<\/annotation><\/semantics><\/math>, guarantees that when the null hypothesis is false, you will observe data that will yield statistically significant results <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b2<\/mi><annotation encoding=\"application\/x-tex\">\\beta<\/annotation><\/semantics><\/math> fraction of the time (a.k.a statistical power), conditional on sample size, effect size of interest, and chosen <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b1<\/mi><annotation encoding=\"application\/x-tex\">\\alpha<\/annotation><\/semantics><\/math> rate. It\u2019s the false negative rate for an experiment with an underlying effect.<\/p><\/li>\n<\/ul>\nHowever, they tell us little about the long-term dynamics of an \u201cexperimentation program\u201d ( a series of experiments, each estimating the impact of a different treatment). You can\u2019t immediately answer questions such as \u201cWhat % of experiments can I expect to detect a statistically significant result in?\u201d.<\/p>\nThat, however, may be an interesting question to answer. Suppose you\u2019re Head of Data Something<\/em> in a company that ran 100 experiments last year, of which only 5% yielded positive statistically significant results (a bit below <a href=\"https:\/\/hdsr.mitpress.mit.edu\/pub\/aj31wj81\/release\/1\">an oft-quoted figure in the industry<\/a>). You may be wondering if that\u2019s normal. Is it really so that 95% of product changes had zero impact? Perhaps they weren\u2019t exactly zero, but simply smaller than you could declare as statistically significant with the sample sizes at hand?<\/p>\nIf you work in a large company, you may find yourself at the opposite of \u201cstatistically but not practically significant.\u201d After all, a conversion rate improvement of 0.5 percentage point may still mean many $$<\/em> annually. However, achieving 80% power on such an effect size requires a 3-4x sample size compared to an effect size of 1 percentage point. Are you willing to make that trade-off and make all experiments 3-4x slower?<\/p>\nUnderstanding the relationship between experiment-level design parameters (minimum detectable effect (MDE), sample size, power) and overall experimentation program win\/detection\/error rates can be pretty illuminating. So, let\u2019s figure out how to do it.<\/p>\nThis blog post is accompanied by an interactive <a href=\"https:\/\/ab-testing-long-term-error-rates.streamlit.app\/\">Streamlit app<\/a> where you can explore how all the different parameters come to life yourself.<\/p>\n<section class=\"level2\" id=\"from-statistical-power-to-detectionwin-rates\">\n<h2 data-anchor-id=\"from-statistical-power-to-detectionwin-rates\">From statistical power to detection\/win rates<\/h2>\nYou may wonder why the answer to long-term detection rates isn\u2019t simply the Type II error rate or \u201cWe will detect (i.e., declare them as statistically significant) differences when they exist 80% of the time\u201d.<\/p>\nThat\u2019s because the Type II error rate is conditional on the MDE. Assuming you use the same MDE all the time, you can claim that when the actual effect is at least as large as the MDE, we will detect it at least 80% of the time.<\/p>\nBut what if it\u2019s not? And how exactly does one think about the \u201cat least\u201d part of the statement? What if actual effects are larger\/smaller than MDE? Your power will be exactly 80% only if you always choose MDE precisely equal to the actual unobservable difference for that specific experiment. If you can do that, you don\u2019t need to experiment!<\/p>\nFrequentist approaches tend to develop tools that find upper\/lower bounds of a measure of interest, and statistical power is an excellent example of that (I saw this clearly articulated in an <a href=\"#0\">essay by Jacob Steinhardt titled \u201cBeyond Bayesians and Frequentists\u201d<\/a> - I highly recommend it).<\/p>\nHowever, we are after an expectation\u2014we want to know the average expected detection rate. For that, we\u2019ll borrow an idea from Bayesian statistics that tends to be better suited for such purposes. Instead of working with fixed values, we will assume a distribution of likely effect sizes (a.k.a., a prior distribution) and then integrate over that distribution to get expected rates.<\/p>\nTo be precise, by integrating over a distribution of hypothesized treatment effect sizes <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>A<\/mi><annotation encoding=\"application\/x-tex\">A<\/annotation><\/semantics><\/math> (we\u2019ll call the distribution <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><msub><mi>F<\/mi><mi>A<\/mi><\/msub><annotation encoding=\"application\/x-tex\">F_A<\/annotation><\/semantics><\/math>), we can calculate:<\/p>\n<ul>\n<li>Expected \u201cdetection rate\u201d - the % of experiments we would declare statistically significant given they have a non-zero actual treatment effect.<\/p><\/li>\n<li>Expected \u201cwin rate\u201d - the % of experiments we would declare statistically significant, but we count only experiments with a positive treatment effect.<\/p><\/li>\n<\/ul>\nMathematically:<\/p>\n<math display=\"block\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mtext mathvariant=\"normal\">expected detection rate<\/mtext><mo>=<\/mo><msubsup><mo>\u222b<\/mo><mrow><mo>\u2212<\/mo><mi>\u221e<\/mi><\/mrow><mrow><mo>+<\/mo><mi>\u221e<\/mi><\/mrow><\/msubsup><msub><mi>F<\/mi><mi>A<\/mi><\/msub><mrow><mo form=\"prefix\" stretchy=\"true\">(<\/mo><mi>x<\/mi><mo form=\"postfix\" stretchy=\"true\">)<\/mo><\/mrow><mtext mathvariant=\"normal\">Pr<\/mtext><mrow><mo form=\"prefix\" stretchy=\"true\">(<\/mo><mrow><mtext mathvariant=\"normal\">reject <\/mtext><mspace width=\"0.333em\"><\/mspace><\/mrow><msub><mi>H<\/mi><mn>0<\/mn><\/msub><mo form=\"prefix\" stretchy=\"false\">|<\/mo><mi>x<\/mi><mo>\u2260<\/mo><mn>0<\/mn><mo>,<\/mo><mi>\u03b1<\/mi><mo>,<\/mo><mi>n<\/mi><mo>,<\/mo><msup><mi>\u03c3<\/mi><mn>2<\/mn><\/msup><mo form=\"postfix\" stretchy=\"true\">)<\/mo><\/mrow><mi>d<\/mi><mi>x<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">\n\n\\text{expected detection rate} = \\int_{-\\infty}^{+\\infty} F_A(x) \\text{Pr}(\\text{reject } H_0 | x \\neq 0, \\alpha, n, \\sigma^2) dx\n\n<\/annotation><\/semantics><\/math> <math display=\"block\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mtext mathvariant=\"normal\">expected win rate<\/mtext><mo>=<\/mo><msubsup><mo>\u222b<\/mo><mn>0<\/mn><mrow><mo>+<\/mo><mi>\u221e<\/mi><\/mrow><\/msubsup><msub><mi>F<\/mi><mi>A<\/mi><\/msub><mrow><mo form=\"prefix\" stretchy=\"true\">(<\/mo><mi>x<\/mi><mo form=\"postfix\" stretchy=\"true\">)<\/mo><\/mrow><mtext mathvariant=\"normal\">Pr<\/mtext><mrow><mo form=\"prefix\" stretchy=\"true\">(<\/mo><mrow><mtext mathvariant=\"normal\">reject <\/mtext><mspace width=\"0.333em\"><\/mspace><\/mrow><msub><mi>H<\/mi><mn>0<\/mn><\/msub><mo form=\"prefix\" stretchy=\"false\">|<\/mo><mi>x<\/mi><mo>\u2260<\/mo><mn>0<\/mn><mo>,<\/mo><mi>n<\/mi><mo>,<\/mo><mi>\u03b1<\/mi><mo>,<\/mo><msup><mi>\u03c3<\/mi><mn>2<\/mn><\/msup><mo form=\"postfix\" stretchy=\"true\">)<\/mo><\/mrow><mi>d<\/mi><mi>x<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">\n\n\\text{expected win rate} = \\int_0^{+\\infty} F_A(x) \\text{Pr}(\\text{reject } H_0 | x \\neq 0, n, \\alpha, \\sigma^2) dx\n\n<\/annotation><\/semantics><\/math><\/p>\nwhere <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>n<\/mi><annotation encoding=\"application\/x-tex\">n<\/annotation><\/semantics><\/math> is the sample size, <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><msup><mi>\u03c3<\/mi><mn>2<\/mn><\/msup><annotation encoding=\"application\/x-tex\">\\sigma^2<\/annotation><\/semantics><\/math> is the variance of the outcome metric, and <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b1<\/mi><annotation encoding=\"application\/x-tex\">\\alpha<\/annotation><\/semantics><\/math> is the chosen statistical significance level.<\/p>\nOne immediate thing to note is that detection\/win rates do not depend on <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>M<\/mi><mi>D<\/mi><mi>E<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">MDE<\/annotation><\/semantics><\/math> or <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b2<\/mi><annotation encoding=\"application\/x-tex\">\\beta<\/annotation><\/semantics><\/math> (statistical power). The only levers you have to increase them are larger samples, variance reduction techniques (e.g., by using covariates, such as CUPED-like methods), better treatments (changes in <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><msub><mi>F<\/mi><mi>A<\/mi><\/msub><annotation encoding=\"application\/x-tex\">F_A<\/annotation><\/semantics><\/math>), and different <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b1<\/mi><annotation encoding=\"application\/x-tex\">\\alpha<\/annotation><\/semantics><\/math> levels.<\/p>\nIf your immediate reaction is, \u201cOMG, a subjective assumption on effect size distribution (gasp!)\u201d, I would like to remind you that choosing the MDE threshold for power calculations is subjective, too. Ideally, one would pick an MDE upfront and tune the sample size to achieve the desired statistical power. However, in practice, <a href=\"https:\/\/aurimas.eu\/blog\/2023\/02\/getting-faster-to-decisions-in-a-b-tests-part-2-misinterpretations-and-practical-challenges-of-classical-hypothesis-testing\/\">there\u2019s always an incentive to cherry-pick an MDE itself<\/a>, given a fixed sample size. It looks better to report a power of 80% with an MDE of 0.015 than a power of 50% with an MDE of 0.01.<\/p>\nHaving said that, if you use MDE correctly and report statistical power together with the MDE, bad MDE choices are more transparent than flawed effect size distribution assumptions. But if we want to estimate average detection\/win rates, that\u2019s the cost we need to pay - there\u2019s no free lunch!<\/p>\n<section class=\"level3\" id=\"selecting-an-effect-size-distribution\">\n<h3 data-anchor-id=\"selecting-an-effect-size-distribution\">Selecting an effect size distribution<\/h3>\nSo, how do we select a potential effect size distribution?<\/p>\n<ul>\n<li>Historical experiment results could be a good starting point for understanding where most of the distribution density should be. If most experiments see effect sizes of a few percentage points, then the distribution should reflect that, too. Importantly, we should consider observed treatment effects in all experiments, not just the statistically significant ones.<\/p><\/li>\n<li>Teams have domain expertise and are paid to do their work, so we\u2019d hope they do better than a random flip of a coin - the mean\/median of the distribution should be positive.<\/p><\/li>\n<li>The distribution should not be symmetric. Teams may occasionally experiment with more drastic changes, but safeguards (dogfooding, small-scale pilots, UX research) should prevent ideas with a major negative impact from reaching the experimentation phase.<\/p><\/li>\n<\/ul>\nFrom a modeling perspective, a (shifted\/scaled) Gamma distribution is a good option for modeling such skewed distributions with limited downside and a longer tail of positive effects.<\/p>\nFor the sake of this blog post, I will use two distributions representing different hypothetical teams:<\/p>\n<ul>\n<li>Mature product team<\/strong>. This team manages a mature product with a well-established user base. Experiments usually represent incremental changes and minor features, and thus, an average experiment achieves just a <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mn>0.5<\/mn><annotation encoding=\"application\/x-tex\">0.5<\/annotation><\/semantics><\/math> percentage point improvement. They extensively rely on dogfooding, UXR, and other means to test ideas before they reach the experimentation phase, thus protecting the downside very well. We\u2019ll model it with a Gamma distribution.<\/p><\/li>\n<li>Startup product team.<\/strong> This team is still in the product-market fit stage. They tend to make wide-ranging changes and like moving fast, without too much research before the experimentation phase. Because it\u2019s a less polished product, experiments tend to produce higher gains (<math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mn>2.0<\/mn><annotation encoding=\"application\/x-tex\">2.0<\/annotation><\/semantics><\/math> percentage point improvements, on average), but they also see negative impacts from changes tested more often. We\u2019ll model it with a Normal distribution.<\/p><\/li>\n<\/ul>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb1\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb1-1\" tabindex=\"-1\"><\/a>eff_size_distrs =<\/span> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-2\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-3\" tabindex=\"-1\"><\/a> name =<\/span> 'Startup team'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-4\" tabindex=\"-1\"><\/a> distr =<\/span> 'x ~ N(0.02, 0.03)'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-5\" tabindex=\"-1\"><\/a> generator =<\/span> function<\/span>() rnorm<\/span>(1<\/span>, mean=<\/span>0.02<\/span>, sd=<\/span>0.03<\/span>),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-6\" tabindex=\"-1\"><\/a> density =<\/span> function<\/span>(x) dnorm<\/span>(x, mean =<\/span> 0.02<\/span>, sd =<\/span> 0.03<\/span>) <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-7\" tabindex=\"-1\"><\/a> ),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-8\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-9\" tabindex=\"-1\"><\/a> name =<\/span> 'Mature team'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-10\" tabindex=\"-1\"><\/a> distr =<\/span> 'x ~ Gamma(2,200)) - 0.005'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-11\" tabindex=\"-1\"><\/a> generator =<\/span> function<\/span>() rgamma<\/span>(1<\/span>, shape=<\/span>2<\/span>, rate =<\/span> 200<\/span>) -<\/span> 0.005<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-12\" tabindex=\"-1\"><\/a> density =<\/span> function<\/span>(x) dgamma<\/span>(x +<\/span> 0.005<\/span>, shape=<\/span>2<\/span>, rate =<\/span> 200<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-13\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-14\" tabindex=\"-1\"><\/a>)<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<\/div>\nHere\u2019s how that looks visually, with some summary statistics below.<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb2\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb2-1\" tabindex=\"-1\"><\/a>emp =<\/span> lapply<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-2\" tabindex=\"-1\"><\/a> eff_size_distrs,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-3\" tabindex=\"-1\"><\/a> function<\/span>(d) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-4\" tabindex=\"-1\"><\/a> r =<\/span> list<\/span>()<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-5\" tabindex=\"-1\"><\/a> r[[d$<\/span>name]] =<\/span> sapply<\/span>(1<\/span>:<\/span>50000<\/span>, function<\/span>(i) d$<\/span>generator<\/span>())<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-6\" tabindex=\"-1\"><\/a> r<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-7\" tabindex=\"-1\"><\/a> }<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-8\" tabindex=\"-1\"><\/a>) |><\/span> bind_cols<\/span>() |><\/span> pivot_longer<\/span>(everything<\/span>(), names_to =<\/span> \"Assumed distribution\"<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-9\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-10\" tabindex=\"-1\"><\/a>ggplot<\/span>(emp) +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-11\" tabindex=\"-1\"><\/a> #geom_density(aes(x=value, color=`Assumed distribution`)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-12\" tabindex=\"-1\"><\/a> sapply<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-13\" tabindex=\"-1\"><\/a> eff_size_distrs, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-14\" tabindex=\"-1\"><\/a> function<\/span>(d) stat_function<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-15\" tabindex=\"-1\"><\/a> aes<\/span>(color=<\/span>d$<\/span>name, fill=<\/span>d$<\/span>name), <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-16\" tabindex=\"-1\"><\/a> fun =<\/span> d$<\/span>density,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-17\" tabindex=\"-1\"><\/a> data =<\/span> tibble<\/span>(name =<\/span> d$<\/span>name, full_name =<\/span> paste<\/span>(d$<\/span>name, ''<\/span>, d$<\/span>distr)),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-18\" tabindex=\"-1\"><\/a> geom=<\/span>\"area\"<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-19\" tabindex=\"-1\"><\/a> alpha=<\/span>0.5<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-20\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-21\" tabindex=\"-1\"><\/a>) +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-22\" tabindex=\"-1\"><\/a> xlim<\/span>(-<\/span>0.05<\/span>, 0.1<\/span>) +<\/span> labs<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-23\" tabindex=\"-1\"><\/a> y=<\/span>'Density'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-24\" tabindex=\"-1\"><\/a> color=<\/span>'Assumed effect size distribution'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-25\" tabindex=\"-1\"><\/a> x=<\/span>'Effect size'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-26\" tabindex=\"-1\"><\/a> title=<\/span>'Some possible effect size assumptions'<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-27\" tabindex=\"-1\"><\/a> ) +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-28\" tabindex=\"-1\"><\/a> theme_light<\/span>() +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-29\" tabindex=\"-1\"><\/a> theme<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-30\" tabindex=\"-1\"><\/a> panel.grid.major =<\/span> element_blank<\/span>(),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-31\" tabindex=\"-1\"><\/a> legend.position =<\/span> \"none\"<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-32\" tabindex=\"-1\"><\/a> ) +<\/span> facet_wrap<\/span>(~<\/span>full_name) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-33\" tabindex=\"-1\"><\/a> geom_vline<\/span>(xintercept =<\/span> 0<\/span>, linetype =<\/span> 'dotted'<\/span>)<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<div>\n<figure class=\"figure\">\n<img decoding=\"async\" class=\"img-fluid figure-img\" 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width=\"768\"\/><\/p>\n<\/figure>\n<\/div>\n<\/div>\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb3\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb3-1\" tabindex=\"-1\"><\/a>emp |><\/span> group_by<\/span>(`<\/span>Assumed distribution<\/span>`<\/span>) |><\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-2\" tabindex=\"-1\"><\/a> summarize<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-3\" tabindex=\"-1\"><\/a> `<\/span>Avg. effect<\/span>`<\/span> =<\/span> mean<\/span>(value),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-4\" tabindex=\"-1\"><\/a> `<\/span>10th pctile<\/span>`<\/span> =<\/span> quantile<\/span>(value, 0.1<\/span>),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-5\" tabindex=\"-1\"><\/a> `<\/span>Q50 (median)<\/span>`<\/span> =<\/span> quantile<\/span>(value, 0.5<\/span>),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-6\" tabindex=\"-1\"><\/a> `<\/span>90th pctile<\/span>`<\/span> =<\/span> quantile<\/span>(value, 0.9<\/span>),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-7\" tabindex=\"-1\"><\/a> `<\/span>Share of experiments with positive effect<\/span>`<\/span> =<\/span> mean<\/span>(if_else<\/span>(value ><\/span> 0<\/span>,1<\/span>,0<\/span>))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-8\" tabindex=\"-1\"><\/a> ) |><\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-9\" tabindex=\"-1\"><\/a> arrange<\/span>(-<\/span>`<\/span>Avg. effect<\/span>`<\/span>) |><\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-10\" tabindex=\"-1\"><\/a> kbl<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-11\" tabindex=\"-1\"><\/a> caption =<\/span> 'Summary statistics of assumed effect size distributions'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-12\" tabindex=\"-1\"><\/a> digits=<\/span>4<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-13\" tabindex=\"-1\"><\/a> ) |><\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-14\" tabindex=\"-1\"><\/a> kable_styling<\/span>()<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<table class=\"table table-sm table-striped small\" data-quarto-postprocess=\"true\">\n<caption>Summary statistics of assumed effect size distributions<\/caption>\n<thead>\n<tr class=\"header\">\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: left;\">Assumed distribution<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">Avg. effect<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">10th pctile<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">Q50 (median)<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">90th pctile<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">Share of experiments with positive effect<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td style=\"text-align: left;\">Startup team<\/td>\n<td style=\"text-align: right;\">0.0201<\/td>\n<td style=\"text-align: right;\">-0.0180<\/td>\n<td style=\"text-align: right;\">0.0199<\/td>\n<td style=\"text-align: right;\">0.0584<\/td>\n<td style=\"text-align: right;\">0.7505<\/td>\n<\/tr>\n<tr class=\"even\">\n<td style=\"text-align: left;\">Mature team<\/td>\n<td style=\"text-align: right;\">0.0050<\/td>\n<td style=\"text-align: right;\">-0.0023<\/td>\n<td style=\"text-align: right;\">0.0034<\/td>\n<td style=\"text-align: right;\">0.0143<\/td>\n<td style=\"text-align: right;\">0.7366<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\nNote that both teams are expected to produce nearly the same (~75%) share of experiments that have a positive impact. We could interpret that as both teams having a similar \u201cquality of ideas.\u201d It\u2019s just that the effect sizes differ due to the different contexts in which they operate - a median experiment of the mature team is assumed to produce just a 0.3ppt effect, while the startup team - 2ppt, a 6x difference.<\/p>\n<\/section>\n<section class=\"level3\" id=\"estimating-average-expected-detectionwin-rates\">\n<h3 data-anchor-id=\"estimating-average-expected-detectionwin-rates\">Estimating average expected detection\/win rates<\/h3>\nTo estimate detection\/win rates, we need a few more inputs. Let\u2019s assume that:<\/p>\n<ul>\n<li>Both teams are optimizing a metric that has a baseline of <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mn>0.17<\/mn><annotation encoding=\"application\/x-tex\">0.17<\/annotation><\/semantics><\/math><\/p><\/li>\n<li>Both teams run experiments with 10,000 observations per group (let\u2019s say they both used <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mn>0.015<\/mn><annotation encoding=\"application\/x-tex\">0.015<\/annotation><\/semantics><\/math> as their MDE and chose <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>\u03b2<\/mi><mo>=<\/mo><mn>0.2<\/mn><\/mrow><annotation encoding=\"application\/x-tex\">\\beta=0.2<\/annotation><\/semantics><\/math>; if you were to plug these inputs into a sample size calculation, you\u2019d get close to 10,000).<\/p><\/li>\n<\/ul>\n<div class=\"cell\">\n<details class=\"code-fold\" open=\"\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb4\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb4-1\" tabindex=\"-1\"><\/a>baseline =<\/span> 0.17<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb4-2\" tabindex=\"-1\"><\/a>required_sample_size =<\/span> 10000<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb4-3\" tabindex=\"-1\"><\/a>mde =<\/span> 0.015<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb4-4\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb4-5\" tabindex=\"-1\"><\/a>#check that power is ~80%<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb4-6\" tabindex=\"-1\"><\/a>power.prop.test<\/span>(n=<\/span>required_sample_size, p1=<\/span>baseline, p2=<\/span>baseline +<\/span> mde)<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output cell-output-stdout\">\n<pre><code>\n\n Two-sample comparison of proportions power calculation \n\n\n\n n = 10000\n\n p1 = 0.17\n\n p2 = 0.185\n\n sig.level = 0.05\n\n power = 0.7927863\n\n alternative = two.sided\n\n\n\nNOTE: n is number in *each* group<\/code><\/pre>\n<\/div>\n<\/div>\nWhat long-term detection rates should they expect to see?<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\" open=\"\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb6\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb6-1\" tabindex=\"-1\"><\/a>estimate_detection_rate =<\/span> function<\/span>(baseline, required_sample_size, density_func) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-2\" tabindex=\"-1\"><\/a> integrate<\/span>(function<\/span>(x) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-3\" tabindex=\"-1\"><\/a> p =<\/span> power.prop.test<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-4\" tabindex=\"-1\"><\/a> n=<\/span>required_sample_size, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-5\" tabindex=\"-1\"><\/a> p1=<\/span>baseline,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-6\" tabindex=\"-1\"><\/a> p2=<\/span>baseline +<\/span> x<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-7\" tabindex=\"-1\"><\/a> )$<\/span>power <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-8\" tabindex=\"-1\"><\/a> density_func<\/span>(x) *<\/span> replace_na<\/span>(p, 0<\/span>) <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-9\" tabindex=\"-1\"><\/a> }, -<\/span>Inf<\/span>, Inf<\/span>)$<\/span>value <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-10\" tabindex=\"-1\"><\/a>}<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-11\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-12\" tabindex=\"-1\"><\/a>tibble<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-13\" tabindex=\"-1\"><\/a> `<\/span>Assumed effect size distribution<\/span>`<\/span> =<\/span> sapply<\/span>(eff_size_distrs, function<\/span>(x) x$<\/span>name),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-14\" tabindex=\"-1\"><\/a> `<\/span>P(detect | effect size distribution, N, baseline)<\/span>`<\/span> =<\/span> sapply<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-15\" tabindex=\"-1\"><\/a> eff_size_distrs,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-16\" tabindex=\"-1\"><\/a> function<\/span>(x) estimate_detection_rate<\/span>(baseline, required_sample_size, x$<\/span>density)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-17\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-18\" tabindex=\"-1\"><\/a>) |><\/span> kbl<\/span>(caption=<\/span>'Long-run detection rates'<\/span>, digits=<\/span>2<\/span>) |><\/span> kable_styling<\/span>()<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<table class=\"table table-sm table-striped small\" data-quarto-postprocess=\"true\">\n<caption>Long-run detection rates<\/caption>\n<thead>\n<tr class=\"header\">\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: left;\">Assumed effect size distribution<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">P(detect | effect size distribution, N, baseline)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td style=\"text-align: left;\">Startup team<\/td>\n<td style=\"text-align: right;\">0.78<\/td>\n<\/tr>\n<tr class=\"even\">\n<td style=\"text-align: left;\">Mature team<\/td>\n<td style=\"text-align: right;\">0.25<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\nA massive difference! The startup team will be detecting an impact in nearly 80% of experiments, while the mature team will do so only in 25% of them!<\/p>\nWhat about win rates?<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\" open=\"\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb7\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb7-1\" tabindex=\"-1\"><\/a>estimate_win_rate =<\/span> function<\/span>(baseline, required_sample_size, density_func) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-2\" tabindex=\"-1\"><\/a> integrate<\/span>(function<\/span>(x) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-3\" tabindex=\"-1\"><\/a> p =<\/span> power.prop.test<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-4\" tabindex=\"-1\"><\/a> n=<\/span>required_sample_size, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-5\" tabindex=\"-1\"><\/a> p1=<\/span>baseline,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-6\" tabindex=\"-1\"><\/a> p2=<\/span>baseline +<\/span> x<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-7\" tabindex=\"-1\"><\/a> )$<\/span>power <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-8\" tabindex=\"-1\"><\/a> density_func<\/span>(x) *<\/span> replace_na<\/span>(p, 0<\/span>) <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-9\" tabindex=\"-1\"><\/a> }, 0<\/span>, Inf<\/span>)$<\/span>value <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-10\" tabindex=\"-1\"><\/a>}<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-11\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-12\" tabindex=\"-1\"><\/a>tibble<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-13\" tabindex=\"-1\"><\/a> `<\/span>Assumed effect size distribution<\/span>`<\/span> =<\/span> sapply<\/span>(eff_size_distrs, function<\/span>(x) x$<\/span>name),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-14\" tabindex=\"-1\"><\/a> `<\/span>P(detect a win | effect size distribution, N, baseline)<\/span>`<\/span> =<\/span> sapply<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-15\" tabindex=\"-1\"><\/a> eff_size_distrs,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-16\" tabindex=\"-1\"><\/a> function<\/span>(x) estimate_win_rate<\/span>(baseline, required_sample_size, x$<\/span>density)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-17\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-18\" tabindex=\"-1\"><\/a>) |><\/span> kbl<\/span>(caption=<\/span>'Long-run win rates'<\/span>, digits=<\/span>2<\/span>) |><\/span> kable_styling<\/span>()<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<table class=\"table table-sm table-striped small\" data-quarto-postprocess=\"true\">\n<caption>Long-run win rates<\/caption>\n<thead>\n<tr class=\"header\">\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: left;\">Assumed effect size distribution<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">P(detect a win | effect size distribution, N, baseline)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td style=\"text-align: left;\">Startup team<\/td>\n<td style=\"text-align: right;\">0.62<\/td>\n<\/tr>\n<tr class=\"even\">\n<td style=\"text-align: left;\">Mature team<\/td>\n<td style=\"text-align: right;\">0.23<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\nWe get 62% and 24%, respectively. The latter number isn\u2019t far off an <a href=\"https:\/\/hdsr.mitpress.mit.edu\/pub\/aj31wj81\/release\/1\">often-quoted<\/a> figure for the win rate in online experiments of <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mn>10<\/mn><mo>\u2212<\/mo><mn>20<\/mn><mi>%<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">10-20\\%<\/annotation><\/semantics><\/math>. That\u2019s worth pondering: we know that the mature team produces winning ideas 75% of the time, but because most effects are minor, the actual observable win rate is just 24%.<\/p>\nRemember, these results were obtained using a sample size that \u201cprovides 80% power under an MDE of 1.5%\u201d! But our \u201cactual power\u201d is closer to 30% (0.24\/0.75). The MDE choice in the case of the mature team is clearly questionable.<\/p>\n<\/section>\n<\/section>\n<section class=\"level2\" id=\"impact-of-sample-size\">\n<h2 data-anchor-id=\"impact-of-sample-size\">Impact of sample size<\/h2>\nHow do win and detection rates change with the sample size? We can plot win and detection rates as a function of <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>n<\/mi><annotation encoding=\"application\/x-tex\">n<\/annotation><\/semantics><\/math>.<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb8\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb8-1\" tabindex=\"-1\"><\/a>sample_sizes =<\/span> seq<\/span>(1000<\/span>, 200000<\/span>, 2000<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-2\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-3\" tabindex=\"-1\"><\/a>win_and_detection_rates =<\/span> pblapply<\/span>(sample_sizes, function<\/span>(n) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-4\" tabindex=\"-1\"><\/a> lapply<\/span>(eff_size_distrs, function<\/span>(d) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-5\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-6\" tabindex=\"-1\"><\/a> name =<\/span> d$<\/span>name, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-7\" tabindex=\"-1\"><\/a> detection_rate =<\/span> estimate_detection_rate<\/span>(baseline, n, d$<\/span>density),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-8\" tabindex=\"-1\"><\/a> win_rate =<\/span> estimate_win_rate<\/span>(baseline, n, d$<\/span>density), <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-9\" tabindex=\"-1\"><\/a> n=<\/span>n<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-10\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-11\" tabindex=\"-1\"><\/a> })<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-12\" tabindex=\"-1\"><\/a>}, cl=<\/span>4<\/span>) |><\/span> bind_rows<\/span>()<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-13\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-14\" tabindex=\"-1\"><\/a>mde_sample_sizes =<\/span> lapply<\/span>(c<\/span>(0.004<\/span>, 0.005<\/span>, 0.0075<\/span>, 0.01<\/span>, 0.015<\/span>), function<\/span>(mde) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-15\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-16\" tabindex=\"-1\"><\/a> n=<\/span>power.prop.test<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-17\" tabindex=\"-1\"><\/a> p1=<\/span>baseline, p2=<\/span>baseline +<\/span> mde, power=<\/span>0.8<\/span>, sig.level=<\/span>0.05<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-18\" tabindex=\"-1\"><\/a> )$<\/span>n, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-19\" tabindex=\"-1\"><\/a> mde=<\/span>mde<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-20\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-21\" tabindex=\"-1\"><\/a>}) |><\/span> bind_rows<\/span>()<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-22\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-23\" tabindex=\"-1\"><\/a>ggplot<\/span>(win_and_detection_rates) +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-24\" tabindex=\"-1\"><\/a> geom_line<\/span>(aes<\/span>(x=<\/span> n, y=<\/span>detection_rate, color=<\/span>name, linetype=<\/span>\"Detection rate\"<\/span>)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-25\" tabindex=\"-1\"><\/a> geom_line<\/span>(aes<\/span>(x=<\/span> n, y=<\/span>win_rate, color=<\/span>name, linetype=<\/span>'Win rate'<\/span>)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-26\" tabindex=\"-1\"><\/a> geom_hline<\/span>(aes<\/span>(yintercept=<\/span>0.75<\/span>, color=<\/span>'Startup team'<\/span>, linetype=<\/span>'Share of positive-impact experiments'<\/span>)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-27\" tabindex=\"-1\"><\/a> geom_hline<\/span>(aes<\/span>(yintercept=<\/span>0.74<\/span>, color=<\/span>'Mature team'<\/span>, linetype=<\/span>'Share of positive-impact experiments'<\/span>)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-28\" tabindex=\"-1\"><\/a> theme_light<\/span>() +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-29\" tabindex=\"-1\"><\/a> theme<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-30\" tabindex=\"-1\"><\/a> panel.grid.major =<\/span> element_blank<\/span>(),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-31\" tabindex=\"-1\"><\/a> legend.position =<\/span> \"right\"<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-32\" tabindex=\"-1\"><\/a> ) +<\/span> labs<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-33\" tabindex=\"-1\"><\/a> x =<\/span> 'Sample size (per group)'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-34\" tabindex=\"-1\"><\/a> y =<\/span> ''<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-35\" tabindex=\"-1\"><\/a> color =<\/span> 'Effect size prior assumption'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-36\" tabindex=\"-1\"><\/a> title =<\/span> 'Avg detection and win rates as function of sample size'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-37\" tabindex=\"-1\"><\/a> linetype =<\/span> 'Metric'<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-38\" tabindex=\"-1\"><\/a> ) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-39\" tabindex=\"-1\"><\/a> scale_x_continuous<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-40\" tabindex=\"-1\"><\/a> sec.axis =<\/span> dup_axis<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-41\" tabindex=\"-1\"><\/a> name =<\/span> 'MDE that results in 80% power'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-42\" tabindex=\"-1\"><\/a> breaks =<\/span> mde_sample_sizes[['n'<\/span>]],<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-43\" tabindex=\"-1\"><\/a> labels =<\/span> scales::<\/span>percent<\/span>(mde_sample_sizes[['mde'<\/span>]]),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-44\" tabindex=\"-1\"><\/a> guide =<\/span> guide_axis<\/span>(n.dodge =<\/span> 3<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-45\" tabindex=\"-1\"><\/a> ),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-46\" tabindex=\"-1\"><\/a> breaks =<\/span> c<\/span>(1000<\/span>, seq<\/span>(0<\/span>, 200000<\/span>, 5000<\/span>)),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-47\" tabindex=\"-1\"><\/a> labels =<\/span> c<\/span>(\"1,000\"<\/span>, sapply<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-48\" tabindex=\"-1\"><\/a> seq<\/span>(0<\/span>, 200000<\/span>, 5000<\/span>), <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-49\" tabindex=\"-1\"><\/a> function<\/span>(x) if_else<\/span>(x %%<\/span> 40000<\/span> ==<\/span> 0<\/span> &&<\/span> x ><\/span> 0<\/span>, format<\/span>(x, big.mark =<\/span> \",\"<\/span>), \"\"<\/span>))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-50\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-51\" tabindex=\"-1\"><\/a> ) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-52\" tabindex=\"-1\"><\/a> scale_y_continuous<\/span>(breaks =<\/span> seq<\/span>(0<\/span>, 1<\/span>, 0.1<\/span>), labels =<\/span> scales::<\/span>percent_format<\/span>()) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-53\" tabindex=\"-1\"><\/a> scale_linetype_manual<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-54\" tabindex=\"-1\"><\/a> values =<\/span> c<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-55\" tabindex=\"-1\"><\/a> \"Detection rate\"<\/span>=<\/span>\"solid\"<\/span>, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-56\" tabindex=\"-1\"><\/a> \"Win rate\"<\/span> =<\/span> 'longdash'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-57\" tabindex=\"-1\"><\/a> \"Share of positive-impact experiments\"<\/span> =<\/span> 'dashed'<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-58\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-59\" tabindex=\"-1\"><\/a> )<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<div>\n<figure class=\"figure\">\n<img decoding=\"async\" class=\"img-fluid figure-img\" 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width=\"768\"\/><\/p>\n<\/figure>\n<\/div>\n<\/div>\n<\/div>\nThere\u2019s a lot going on in this chart, but here\u2019s a few key takeaways:<\/p>\n<ul>\n<li>The start-up team reaches ~90% detection rates (and, by extension, approaches its true win rate) as soon as the experiment sample size exceeds 40,000 observations per group. There\u2019s little to be gained beyond that.<\/p><\/li>\n<li>At the same time, if this were an early-stage start-up with just a few thousand customers to experiment with, it\u2019s very possible it would see win\/detection rates of just 50-60%. It\u2019s a tough trade-off between the speed in decision-making that\u2019s key in a start-up context and a higher share of false positives.<\/p><\/li>\n<li>In the case of the mature team, there\u2019s most to be gained until sample sizes reach ~80,000 observations per group. That\u2019s an MDE of ~0.5%! Previously, we saw that using a 1.5% MDE (or 10,000 users per group) yields an observable win rate of 24%; increasing the sample size to 80,000 would double the observable win rate. Granted, most of these wins would be small, but as discussed above, that may still be a lot of money.<\/p><\/li>\n<li>The mature team would have difficulty seeing win rates close to its true success rate (74%). It would take 5,000,000 observations per group to reach a win rate of 70%.<\/p><\/li>\n<\/ul>\n<\/section>\n<section class=\"level2\" id=\"what-about-the-type-i-error-rate-in-this-context\">\n<h2 data-anchor-id=\"what-about-the-type-i-error-rate-in-this-context\">What about the Type I error rate in this context?<\/h2>\nWe saw how to go from statistical power \/ Type II error rate to long-term detection\/win rates. Can we do the same with the Type I error rate?<\/p>\nWell, the thing is that the Type I error rate is only relevant if some experiments have zero impact. It\u2019s not \u201cclose enough\u201d to zero, but literally zero. In reality, (hopefully!) you don\u2019t run such experiments. It works for individual experiments because the frequentist hypothesis testing framework asks \u201chow likely we would have observed data like the one at hand if the true effect was zero\u201d and not \u201chow likely the true effect is zero.\u201d But this question doesn\u2019t translate to a series of experiments.<\/p>\nAdditionally, the Type I error rate does not depend on the data you collect (sample size, variance). Even if you assume that some experiments have precisely zero impact, all you can do is choose Type I error rate with <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b1<\/mi><annotation encoding=\"application\/x-tex\">\\alpha<\/annotation><\/semantics><\/math>; you can\u2019t influence it beyond that choice.<\/p>\nAs a result, there\u2019s nothing we can optimize \u00af\\_(\u30c4)_\/\u00af. You could ask deeper questions about the usefulness of an independent-of-data error rate that doesn\u2019t apply in most situations, which would land you into the world of \u201cIs null hypothesis testing the right thing to do?\u201d but we\u2019re not going there today.<\/p>\nWhat are the alternatives?<\/p>\nFor one, we can estimate expected confidence interval widths and quantify the average uncertainty in our results. In a <a href=\"https:\/\/stats.stackexchange.com\/questions\/240452\/does-the-width-of-the-confidence-interval-necessarily-imply-power\">simple t-test setting<\/a>: <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>C<\/mi><msub><mi>I<\/mi><mtext mathvariant=\"normal\">width<\/mtext><\/msub><mo>=<\/mo><mn>2<\/mn><mi>M<\/mi><mi>D<\/mi><mi>E<\/mi><mrow><mo form=\"prefix\" stretchy=\"true\">(<\/mo><mi>\u03b2<\/mi><mo>=<\/mo><mn>0.5<\/mn><mo form=\"postfix\" stretchy=\"true\">)<\/mo><\/mrow><\/mrow><annotation encoding=\"application\/x-tex\">CI_{\\text{width}} = 2 MDE(\\beta=0.5)<\/annotation><\/semantics><\/math>, i.e., the widths are twice the MDE that yields 50% power. Or, graphically:<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb9\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb9-1\" tabindex=\"-1\"><\/a>sample_sizes =<\/span> seq<\/span>(1000<\/span>, 200000<\/span>, 1000<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-2\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-3\" tabindex=\"-1\"><\/a>ci_widths =<\/span> lapply<\/span>(sample_sizes, function<\/span>(n) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-4\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-5\" tabindex=\"-1\"><\/a> n=<\/span>n,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-6\" tabindex=\"-1\"><\/a> ci_width =<\/span> (power.prop.test<\/span>(p1=<\/span>baseline, power=<\/span>0.5<\/span>, n=<\/span>n)$<\/span>p2 -<\/span> baseline) *<\/span> 2<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-7\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-8\" tabindex=\"-1\"><\/a>}) |><\/span> bind_rows<\/span>()<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-9\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-10\" tabindex=\"-1\"><\/a>ggplot<\/span>(ci_widths) +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-11\" tabindex=\"-1\"><\/a> geom_linerange<\/span>(aes<\/span>(x=<\/span>n, ymin=<\/span>-<\/span>ci_width\/<\/span>2<\/span>, ymax=<\/span>ci_width\/<\/span>2<\/span>)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-12\" tabindex=\"-1\"><\/a> theme_light<\/span>() +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-13\" tabindex=\"-1\"><\/a> theme<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-14\" tabindex=\"-1\"><\/a> panel.grid.major =<\/span> element_blank<\/span>(),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-15\" tabindex=\"-1\"><\/a> legend.position =<\/span> \"right\"<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-16\" tabindex=\"-1\"><\/a> ) +<\/span> labs<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-17\" tabindex=\"-1\"><\/a> x =<\/span> 'Sample size (per group)'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-18\" tabindex=\"-1\"><\/a> y =<\/span> 'CI width'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-19\" tabindex=\"-1\"><\/a> title =<\/span> 'Confidence interval widths as a function of sample size'<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-20\" tabindex=\"-1\"><\/a> )<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<div>\n<figure class=\"figure\">\n<img decoding=\"async\" class=\"img-fluid figure-img\" 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\" width=\"768\"\/><\/p>\n<\/figure>\n<\/div>\n<\/div>\n<\/div>\nBut in the spirit of focusing on \u201cerror rates,\u201d let\u2019s explore some other error types - meet Type S and Type M error rates.<\/p>\n<section class=\"level3\" id=\"type-m-and-s-errors\">\n<h3 data-anchor-id=\"type-m-and-s-errors\">Type M and S errors<\/h3>\n<a href=\"https:\/\/journals.sagepub.com\/doi\/10.1177\/1745691614551642\">Gelman and Carlin<\/a> (2014) introduced two other metrics to use in statistical inference:<\/p>\n<ul>\n<li>Type S (sign) error.<\/strong> It\u2019s the probability that an observed difference, when declared statistically significant, has an opposite sign than the actual difference. In other words, it is the probability that you will make a decision that is opposite to what you are after. It directly relates to the practical implications of making a wrong decision. In a lot of situations, rolling out a zero-impact change is not the end of the world. Rolling out something that has an impact opposite to the one estimated in an experiment? That\u2019s bad!<\/li>\n<li>Type M (Magnitude) error<\/strong> measures how much the observed difference, when declared statistically significant, differs from the actual difference, expressed as a ratio. It partially addresses the issue of quantifying the risk of saying \u201ceffect sizes so small that they don\u2019t practically matter\u201d as statistically significant and is closely related to the <a href=\"https:\/\/www.etsy.com\/codeascraft\/mitigating-the-winners-curse-in-online-experiments?utm_source=pocket_reader\">winner\u2019s curse phenomenon<\/a>. I like Type M error a lot because it provides a rigorous quantification of the hand-wavy statement, \u201cYeah, the results are statistically significant, but I would not trust them due to the small sample size.\u201d It provides a bridge between sample size and Type I error rate.<\/li>\n<\/ul>\n<section class=\"level4\" id=\"calculating-type-s-and-m-error-rates\">\n<h4 data-anchor-id=\"calculating-type-s-and-m-error-rates\">Calculating Type S and M error rates<\/h4>\nThe original paper proposed calculating Type S and M error rates via simulation. using a function. In 2019, <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/29569719\/\">Lu, Qui, and Deng<\/a> published a paper that included closed-form formulas for these error rates. Their implementation is below (and can be easily extended to unequal sample size designs).<\/p>\nThe rates are a function of four parameters: <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>A<\/mi><annotation encoding=\"application\/x-tex\">A<\/annotation><\/semantics><\/math>, the effect size; <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03c3<\/mi><annotation encoding=\"application\/x-tex\">\\sigma<\/annotation><\/semantics><\/math>, the observed standard deviation of a metric; <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b1<\/mi><annotation encoding=\"application\/x-tex\">\\alpha<\/annotation><\/semantics><\/math>, the chosen level of statistical significance; and <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>n<\/mi><annotation encoding=\"application\/x-tex\">n<\/annotation><\/semantics><\/math>, the sample size.<\/p>\n<math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>A<\/mi><annotation encoding=\"application\/x-tex\">A<\/annotation><\/semantics><\/math>, conceptually, is a hypothesized<\/em> effect size. That\u2019s not the same as <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>M<\/mi><mi>D<\/mi><mi>E<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">MDE<\/annotation><\/semantics><\/math> - arguably, in a well-designed experiment, <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>M<\/mi><mi>D<\/mi><mi>E<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">MDE<\/annotation><\/semantics><\/math> should always be smaller than <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>A<\/mi><annotation encoding=\"application\/x-tex\">A<\/annotation><\/semantics><\/math>, as it\u2019s the minimum effect size you\u2019re interested in detecting.<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\" open=\"\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb10\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb10-1\" tabindex=\"-1\"><\/a>calculate_type_SM =<\/span> function<\/span>(A, sd, n, alpha =<\/span> 0.05<\/span>) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-2\" tabindex=\"-1\"><\/a> # A - effect size<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-3\" tabindex=\"-1\"><\/a> # sd - baseline standard deviation<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-4\" tabindex=\"-1\"><\/a> # n - size per group<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-5\" tabindex=\"-1\"><\/a> lambda =<\/span> abs<\/span>(A) \/<\/span> sqrt<\/span>(sd**<\/span>2<\/span>\/<\/span>n +<\/span> sd**<\/span>2<\/span>\/<\/span>n)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-6\" tabindex=\"-1\"><\/a> z =<\/span> qnorm<\/span>(1<\/span> -<\/span> alpha\/<\/span>2<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-7\" tabindex=\"-1\"><\/a> neg =<\/span> pnorm<\/span>(-<\/span>z -<\/span>lambda)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-8\" tabindex=\"-1\"><\/a> diff =<\/span> pnorm<\/span>(z -<\/span> lambda)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-9\" tabindex=\"-1\"><\/a> pos =<\/span> pnorm<\/span>(z +<\/span> lambda)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-10\" tabindex=\"-1\"><\/a> inv_diff =<\/span> pnorm<\/span>(lambda -<\/span> z)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-11\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-12\" tabindex=\"-1\"><\/a> power =<\/span> neg +<\/span> 1<\/span> -<\/span> diff,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-13\" tabindex=\"-1\"><\/a> typeS =<\/span> neg \/<\/span> (neg +<\/span> 1<\/span> -<\/span> diff),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-14\" tabindex=\"-1\"><\/a> typeM =<\/span> (<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-15\" tabindex=\"-1\"><\/a> dnorm<\/span>(lambda +<\/span> z) +<\/span> dnorm<\/span>(lambda -<\/span> z) +<\/span> lambda *<\/span> (pos +<\/span> inv_diff -<\/span> 1<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-16\" tabindex=\"-1\"><\/a> ) \/<\/span> (lambda *<\/span> (1<\/span> -<\/span> pos +<\/span> inv_diff))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-17\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-18\" tabindex=\"-1\"><\/a>}<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<\/div>\nTo get a sense of how these error rates look like, let\u2019s use the setup from the examples above: a baseline of 17%,<\/em> the hypothesized effect size of 1 percentage point, and a sample size of 10,000 users in each group:<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\" open=\"\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb11\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb11-1\" tabindex=\"-1\"><\/a>closed_form =<\/span> calculate_type_SM<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb11-2\" tabindex=\"-1\"><\/a> A=<\/span>0.01<\/span>, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb11-3\" tabindex=\"-1\"><\/a> sd=<\/span>sqrt<\/span>(baseline *<\/span> (1<\/span> -<\/span> baseline)), <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb11-4\" tabindex=\"-1\"><\/a> n =<\/span> required_sample_size<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb11-5\" tabindex=\"-1\"><\/a>) |><\/span> as_tibble<\/span>() |><\/span> mutate<\/span>(method =<\/span> 'Closed-form formula'<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb11-6\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb11-7\" tabindex=\"-1\"><\/a>closed_form |><\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb11-8\" tabindex=\"-1\"><\/a> kbl<\/span>(caption =<\/span> 'Estimated Metrics'<\/span>, digits=<\/span>6<\/span>) |><\/span> kable_styling<\/span>()<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<table class=\"table table-sm table-striped small\" data-quarto-postprocess=\"true\">\n<caption>Estimated Metrics<\/caption>\n<thead>\n<tr class=\"header\">\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">power<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">typeS<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">typeM<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: left;\">method<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td style=\"text-align: right;\">0.469165<\/td>\n<td style=\"text-align: right;\">0.00013<\/td>\n<td style=\"text-align: right;\">1.45038<\/td>\n<td style=\"text-align: left;\">Closed-form formula<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\nThe risk of a sign error in such a setting is very small - just <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mo>\u223c<\/mo><mn>1<\/mn><mi>\/<\/mi><mn>7700<\/mn><\/mrow><annotation encoding=\"application\/x-tex\">\\sim1\/7700<\/annotation><\/semantics><\/math>. The type M ratio, on the other hand, is <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mn>1.45<\/mn><annotation encoding=\"application\/x-tex\">1.45<\/annotation><\/semantics><\/math>; a 45% overestimation can be meaningful in decision-making.<\/p>\nWe can verify that these formulas are correct with a small simulation. In this simulation, we repeat the same experiment with the above parameters many times and estimate empirical error rate estimates. Note that estimating Type S error accurately via simulation isn\u2019t easy given how rare it is, which is why even with 100,000 loops, we are still a bit off.<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\" open=\"\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb12\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb12-1\" tabindex=\"-1\"><\/a>pboptions<\/span>(type=<\/span>\"none\"<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-2\" tabindex=\"-1\"><\/a>sim_results =<\/span> pbsapply<\/span>(1<\/span>:<\/span>100000<\/span>, function<\/span>(i) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-3\" tabindex=\"-1\"><\/a> set.seed<\/span>(i*<\/span>24<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-4\" tabindex=\"-1\"><\/a> A =<\/span> rbinom<\/span>(required_sample_size, 1<\/span>, baseline)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-5\" tabindex=\"-1\"><\/a> B =<\/span> rbinom<\/span>(required_sample_size, 1<\/span>, baseline +<\/span> 0.01<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-6\" tabindex=\"-1\"><\/a> mean_diff =<\/span> mean<\/span>(B) -<\/span> mean<\/span>(A)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-7\" tabindex=\"-1\"><\/a> p_value =<\/span> t.test<\/span>(B, A)$<\/span>p.value <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-8\" tabindex=\"-1\"><\/a> c<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-9\" tabindex=\"-1\"><\/a> detected =<\/span> p_value <=<\/span> 0.05<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-10\" tabindex=\"-1\"><\/a> sign_error =<\/span> ifelse<\/span>(p_value <=<\/span> 0.05<\/span>, sign<\/span>(mean_diff) !=<\/span> sign<\/span>(0.01<\/span>), NA<\/span>),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-11\" tabindex=\"-1\"><\/a> ratio =<\/span> ifelse<\/span>(p_value <=<\/span> 0.05<\/span>, mean_diff \/<\/span> 0.01<\/span>, NA<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-12\" tabindex=\"-1\"><\/a> ) <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-13\" tabindex=\"-1\"><\/a>}, cl=<\/span>4<\/span>) |><\/span> t<\/span>() |><\/span> as_tibble<\/span>()<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-14\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-15\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-16\" tabindex=\"-1\"><\/a>empirical_rates =<\/span> sim_results |><\/span> summarize<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-17\" tabindex=\"-1\"><\/a> power =<\/span> mean<\/span>(detected, na.rm=<\/span>T),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-18\" tabindex=\"-1\"><\/a> typeS =<\/span> mean<\/span>(sign_error, na.rm=<\/span>T),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-19\" tabindex=\"-1\"><\/a> typeM =<\/span> mean<\/span>(ratio, na.rm=<\/span>T),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-20\" tabindex=\"-1\"><\/a> method =<\/span> 'Empirical results'<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-21\" tabindex=\"-1\"><\/a>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-22\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-23\" tabindex=\"-1\"><\/a>bind_rows<\/span>(closed_form, empirical_rates) |><\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-24\" tabindex=\"-1\"><\/a> kbl<\/span>(caption =<\/span> 'Estimated Metrics'<\/span>) |><\/span> kable_styling<\/span>()<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<table class=\"table table-sm table-striped small\" data-quarto-postprocess=\"true\">\n<caption>Estimated Metrics<\/caption>\n<thead>\n<tr class=\"header\">\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">power<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">typeS<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">typeM<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: left;\">method<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td style=\"text-align: right;\">0.4691649<\/td>\n<td style=\"text-align: right;\">0.0001298<\/td>\n<td style=\"text-align: right;\">1.450380<\/td>\n<td style=\"text-align: left;\">Closed-form formula<\/td>\n<\/tr>\n<tr class=\"even\">\n<td style=\"text-align: right;\">0.4581100<\/td>\n<td style=\"text-align: right;\">0.0000655<\/td>\n<td style=\"text-align: right;\">1.462017<\/td>\n<td style=\"text-align: left;\">Empirical results<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\nLu, Qiu, and Deng\u2019s paper includes charts that perfectly illustrate the risk associated with underpowered experiments. On the x-axis, we plot statistical power (or, equivalently, sample size given a chosen <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>A<\/mi><mo>=<\/mo><mi>M<\/mi><mi>D<\/mi><mi>E<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">A=MDE<\/annotation><\/semantics><\/math>), and on the y-axis - Type S error rate \/ Type M ratio:<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb13\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb13-1\" tabindex=\"-1\"><\/a>error_rates =<\/span> lapply<\/span>(seq<\/span>(500<\/span>, 40000<\/span>, 500<\/span>), function<\/span>(n) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-2\" tabindex=\"-1\"><\/a> calculate_type_SM<\/span>(0.01<\/span>, sqrt<\/span>(baseline *<\/span> (1<\/span> -<\/span> baseline)), n)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-3\" tabindex=\"-1\"><\/a>}) |><\/span> bind_rows<\/span>() |><\/span> pivot_longer<\/span>(-<\/span>power)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-4\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-5\" tabindex=\"-1\"><\/a>ggplot<\/span>(error_rates, aes<\/span>(x=<\/span>power)) +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-6\" tabindex=\"-1\"><\/a> geom_line<\/span>(aes<\/span>(y=<\/span>value, color=<\/span>name)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-7\" tabindex=\"-1\"><\/a> scale_x_continuous<\/span>(breaks=<\/span>seq<\/span>(0.05<\/span>, 0.95<\/span>, 0.2<\/span>)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-8\" tabindex=\"-1\"><\/a> facet_wrap<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-9\" tabindex=\"-1\"><\/a> ~<\/span>name, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-10\" tabindex=\"-1\"><\/a> scales=<\/span>'free_y'<\/span>, strip.position =<\/span> 'top'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-11\" tabindex=\"-1\"><\/a> labeller =<\/span> as_labeller<\/span>(c<\/span>(typeM =<\/span> 'Type M error ratio'<\/span>, typeS=<\/span>'Type S error rate'<\/span>))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-12\" tabindex=\"-1\"><\/a> ) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-13\" tabindex=\"-1\"><\/a> labs<\/span>(x =<\/span> 'Statistical power'<\/span>) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-14\" tabindex=\"-1\"><\/a> theme_light<\/span>() +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-15\" tabindex=\"-1\"><\/a> theme<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-16\" tabindex=\"-1\"><\/a> panel.grid.major.x =<\/span> element_blank<\/span>(),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-17\" tabindex=\"-1\"><\/a> legend.position =<\/span> \"none\"<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-18\" tabindex=\"-1\"><\/a> strip.placement =<\/span> \"outside\"<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-19\" tabindex=\"-1\"><\/a> ) +<\/span> ylab<\/span>(NULL<\/span>)<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<div>\n<figure class=\"figure\">\n<img decoding=\"async\" class=\"img-fluid figure-img\" 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width=\"768\"\/><\/p>\n<\/figure>\n<\/div>\n<\/div>\n<\/div>\nWe can see that Type S error (which is very costly, decision-making-wise!) largely disappears once we hit the statistical power of 0.35\u22120.4. On the other hand, the type M ratio only goes below 1.5x once we achieve the statistical power of <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mn>0.5<\/mn><annotation encoding=\"application\/x-tex\">0.5<\/annotation><\/semantics><\/math>.<\/p>\nThese charts should be a part of any Experimentation 101 course.<\/p>\n<\/section>\n<\/section>\n<section class=\"level3\" id=\"obtaining-long-term-sign-error-exaggeration-rates\">\n<h3 data-anchor-id=\"obtaining-long-term-sign-error-exaggeration-rates\">Obtaining long-term sign-error \/ exaggeration rates<\/h3>\nType M and S rates depend on a hypothesized effect size <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>A<\/mi><annotation encoding=\"application\/x-tex\">A<\/annotation><\/semantics><\/math>, just like the Type II error rate depends on <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>M<\/mi><mi>D<\/mi><mi>E<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">MDE<\/annotation><\/semantics><\/math>, so we\u2019ll need the same integration approach to get to long-term \u201caverage\u201d rates. We\u2019ll also tweak the definitions a bit and calculate:<\/p>\n<ul>\n<li>Expected \u201csign error rate\u201d - the % of experiments where we will declare the results statistically significant, but the observed treatment effect will have an opposite sign than the actual treatment effect. We\u2019ll use all experiments as the denominator here, which is slightly different from the Type S error rate definition. If needed, we can divide this rate by the average detection rate to get back to the original definition.<\/p><\/li>\n<li>Expected \u201caverage absolute exaggeration\u201d - the expected absolute difference between the observed and actual treatment effects among experiments that we declare statistically significant. We\u2019ll focus on absolute difference rather than a Type M-style ratio metric because integrating Type M ratio directly at small effect sizes is challenging as the ratio\u2019s denominator approaches zero.<\/p><\/li>\n<\/ul>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb14\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb14-1\" tabindex=\"-1\"><\/a>estimate_sign_error_rate =<\/span> function<\/span>(baseline, s, density_func) { <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-2\" tabindex=\"-1\"><\/a> baseline_sd =<\/span> sqrt<\/span>(baseline *<\/span> (1<\/span> -<\/span> baseline))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-3\" tabindex=\"-1\"><\/a> integrate<\/span>(function<\/span>(x) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-4\" tabindex=\"-1\"><\/a> r =<\/span> calculate_type_SM<\/span>(n=<\/span>s, A=<\/span>x, sd=<\/span>baseline_sd)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-5\" tabindex=\"-1\"><\/a> (r$<\/span>typeS *<\/span> r$<\/span>power) *<\/span> density_func<\/span>(x) <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-6\" tabindex=\"-1\"><\/a> }, -<\/span>Inf<\/span>, Inf<\/span>, stop.on.error =<\/span> F)$<\/span>value<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-7\" tabindex=\"-1\"><\/a>}<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-8\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-9\" tabindex=\"-1\"><\/a>estimate_abs_exaggeration =<\/span> function<\/span>(baseline, s, density_func) { <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-10\" tabindex=\"-1\"><\/a> baseline_sd =<\/span> sqrt<\/span>(baseline *<\/span> (1<\/span> -<\/span> baseline))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-11\" tabindex=\"-1\"><\/a> integrate<\/span>(function<\/span>(x) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-12\" tabindex=\"-1\"><\/a> r =<\/span> calculate_type_SM<\/span>(n=<\/span>s, A=<\/span>x, sd=<\/span>baseline_sd)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-13\" tabindex=\"-1\"><\/a> (r$<\/span>typeM -<\/span> 1<\/span>) *<\/span> density_func<\/span>(x) *<\/span> abs<\/span>(x) *<\/span> r$<\/span>power <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-14\" tabindex=\"-1\"><\/a> }, -<\/span>Inf<\/span>, Inf<\/span>, stop.on.error =<\/span> F)$<\/span>value<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-15\" tabindex=\"-1\"><\/a>}<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-16\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-17\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-18\" tabindex=\"-1\"><\/a>sign_and_ratio_rates =<\/span> pblapply<\/span>(sample_sizes, function<\/span>(n) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-19\" tabindex=\"-1\"><\/a> lapply<\/span>(eff_size_distrs, function<\/span>(d) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-20\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-21\" tabindex=\"-1\"><\/a> name =<\/span> d$<\/span>name, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-22\" tabindex=\"-1\"><\/a> sign_error_rate =<\/span> estimate_sign_error_rate<\/span>(baseline, n, d$<\/span>density),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-23\" tabindex=\"-1\"><\/a> exaggeration_ratio =<\/span> estimate_abs_exaggeration<\/span>(baseline, n, d$<\/span>density), <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-24\" tabindex=\"-1\"><\/a> n=<\/span>n<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-25\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-26\" tabindex=\"-1\"><\/a> })<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-27\" tabindex=\"-1\"><\/a> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-28\" tabindex=\"-1\"><\/a>}, cl=<\/span>4<\/span>) |><\/span> bind_rows<\/span>() |><\/span> pivot_longer<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-29\" tabindex=\"-1\"><\/a> cols=<\/span>c<\/span>('sign_error_rate'<\/span>, 'exaggeration_ratio'<\/span>), names_to =<\/span> \"metric\"<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-30\" tabindex=\"-1\"><\/a>)<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<\/div>\nHow do these rates look under the assumptions of effect size distributions we used earlier?<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb15\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb15-1\" tabindex=\"-1\"><\/a>ggplot<\/span>(sign_and_ratio_rates) +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-2\" tabindex=\"-1\"><\/a> geom_line<\/span>(aes<\/span>(x=<\/span> n, y=<\/span>value, color=<\/span>name)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-3\" tabindex=\"-1\"><\/a> theme_light<\/span>() +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-4\" tabindex=\"-1\"><\/a> theme<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-5\" tabindex=\"-1\"><\/a> panel.grid.major =<\/span> element_blank<\/span>(),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-6\" tabindex=\"-1\"><\/a> legend.position =<\/span> \"bottom\"<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-7\" tabindex=\"-1\"><\/a> ) +<\/span> labs<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-8\" tabindex=\"-1\"><\/a> x =<\/span> 'Sample size (per group)'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-9\" tabindex=\"-1\"><\/a> y =<\/span> ''<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-10\" tabindex=\"-1\"><\/a> color =<\/span> 'Effect size prior assumption'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-11\" tabindex=\"-1\"><\/a> title =<\/span> 'Avg sign error rates and effect exaggeration size function of sample size'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-12\" tabindex=\"-1\"><\/a> subtitle=<\/span>'Note: Discontinuities in the charts are an artifact of numeric integration...'<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-13\" tabindex=\"-1\"><\/a> ) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-14\" tabindex=\"-1\"><\/a> facet_wrap<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-15\" tabindex=\"-1\"><\/a> ~<\/span>metric, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-16\" tabindex=\"-1\"><\/a> scales=<\/span>'free_y'<\/span>, strip.position =<\/span> 'top'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-17\" tabindex=\"-1\"><\/a> labeller =<\/span> as_labeller<\/span>(c<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-18\" tabindex=\"-1\"><\/a> exaggeration_ratio =<\/span> 'Avg. absolute exaggeration'<\/span>, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-19\" tabindex=\"-1\"><\/a> sign_error_rate=<\/span>'Avg. sign error rate'<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-20\" tabindex=\"-1\"><\/a> ))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-21\" tabindex=\"-1\"><\/a> ) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-22\" tabindex=\"-1\"><\/a> scale_x_continuous<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-23\" tabindex=\"-1\"><\/a> breaks =<\/span> c<\/span>(1000<\/span>, seq<\/span>(0<\/span>, 200000<\/span>, 5000<\/span>)),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-24\" tabindex=\"-1\"><\/a> labels =<\/span> c<\/span>(\"1,000\"<\/span>, sapply<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-25\" tabindex=\"-1\"><\/a> seq<\/span>(0<\/span>, 200000<\/span>, 5000<\/span>), <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-26\" tabindex=\"-1\"><\/a> function<\/span>(x) if_else<\/span>(x %%<\/span> 40000<\/span> ==<\/span> 0<\/span> &&<\/span> x ><\/span> 0<\/span>, format<\/span>(x, big.mark =<\/span> \",\"<\/span>), \"\"<\/span>))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-27\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-28\" tabindex=\"-1\"><\/a> )<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<div>\n<figure class=\"figure\">\n<img decoding=\"async\" class=\"img-fluid figure-img\" 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width=\"768\"\/><\/p>\n<\/figure>\n<\/div>\n<\/div>\n<\/div>\n<ul>\n<li>Broadly, once you enter 5000+ observations per group territory, sign error rates and average absolute exaggeration become limited. Even at \u201csmall\u201d sample sizes (e.g., 1,000 observations), they are not worrisome, especially in the case of the start-up team.<\/p><\/li>\n<li>The mature team has it worse on both metrics, given the same sample size; its experiments have a higher risk of sign error and a higher exaggeration ratio. This reflects the fact that, when the results are statistically significant, the effect sizes tend to be smaller (and thus at a higher risk of being influenced by a higher sampling error).<\/p><\/li>\n<li>Again, a sample size of 80,000 observations per group seems to be where the mature team starts to see diminishing returns. The average absolute exaggeration decreases below <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mn>0.0005<\/mn><annotation encoding=\"application\/x-tex\">0.0005<\/annotation><\/semantics><\/math> (5 basis points), and the sign error rate goes below <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mn>0.25<\/mn><mi>%<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">0.25\\%<\/annotation><\/semantics><\/math>.<\/p><\/li>\n<\/ul>\n<\/section>\n<\/section>\n<section class=\"level2\" id=\"conclusion\">\n<h2 data-anchor-id=\"conclusion\">Conclusion<\/h2>\nSo, what\u2019s the TLDR?<\/p>\n<ul>\n<li>A low observable win rate of (e.g., <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mn>25<\/mn><mi>%<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">25\\%<\/annotation><\/semantics><\/math>) doesn\u2019t necessarily mean that the team produces winning ideas infrequently. It may simply be that the impact those ideas have tends to be smaller than you can reliably detect.<\/p><\/li>\n<li>The only way to increase observable win\/detection rates is by increasing sample sizes (or reducing the variance of the outcome metric). Choosing a higher MDE will not. If the effect sizes you tend to achieve are ~1ppt, you are artificially reducing your long-term win rate 2x if you run experiments with MDE of 1ppt instead of 0.5ppt.<\/p><\/li>\n<li>Once you are in the 5,000+ sample size territory, the risk of sign errors is minimal. On the other hand, exaggeration ratios tend to remain sizable and should be considered when making decisions.<\/p><\/li>\n<\/ul>\nDoes this mean a team operating in a mature setting should insist on 100,000+ observations in every experiment? It depends on how you answer questions like:<\/p>\n<ul>\n<li>Do small effects truly matter? Perhaps having a 50% chance of detecting a 0.5ppt impact is OK? Maybe a tiny positive effect size is still a failure from a business perspective?<\/p><\/li>\n<li>Do experiment runtimes impede the team\u2019s ability to learn and execute? It\u2019s one thing if you\u2019re just impatient and need to accept that waiting for another four weeks will enable a more precise decision, bringing in <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>$<\/mi><mi>$<\/mi><mi>$<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">\\$\\$\\$<\/annotation><\/semantics><\/math> for years to come. It\u2019s another if you\u2019re blocked by the experiment or run so few experiments that you\u2019re not learning enough.<\/p><\/li>\n<\/ul>\nMost importantly, remember that fiddling with an MDE gives you nothing. Sample size is king. Use CUPED to reduce variance and sequential testing to enable peeking; otherwise, it\u2019s all about the actual changes you test.<\/p>\nAnd, if you want to play around with effect size distrubution assumptions and see corresponding win\/detection\/sign error rates - I made a small <a href=\"https:\/\/ab-testing-long-term-error-rates.streamlit.app\/\">Streamlit app<\/a> just for that.<\/p>\n<\/section>\n<section class=\"level2\" id=\"appendix---dont-trust-the-maths\">\n<h2 data-anchor-id=\"appendix---dont-trust-the-maths\">Appendix - Don\u2019t trust the maths<\/h2>\nIntegration formulas look fancy, but do they work? I ran another simulation to double-check\u2014it looks like it all adds up, with exception of exaggeration ratios that are a bit off. I suspect that differences arise from numerical approximations in integration.<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb16\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb16-1\" tabindex=\"-1\"><\/a>num_experiments =<\/span> 100000<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-2\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-3\" tabindex=\"-1\"><\/a>used_sample_size =<\/span> floor<\/span>(power.prop.test<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-4\" tabindex=\"-1\"><\/a> p1=<\/span>baseline, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-5\" tabindex=\"-1\"><\/a> p2=<\/span>baseline +<\/span> 0.015<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-6\" tabindex=\"-1\"><\/a> power=<\/span>0.2<\/span>)$<\/span>n)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-7\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-8\" tabindex=\"-1\"><\/a>appendix_sim =<\/span> pblapply<\/span>(eff_size_distrs, function<\/span>(d) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-9\" tabindex=\"-1\"><\/a> drawn_effect_sizes =<\/span> sapply<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-10\" tabindex=\"-1\"><\/a> 1<\/span>:<\/span>num_experiments, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-11\" tabindex=\"-1\"><\/a> function<\/span>(i) d$<\/span>generator<\/span>()<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-12\" tabindex=\"-1\"><\/a> ) <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-13\" tabindex=\"-1\"><\/a> set.seed<\/span>(42<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-14\" tabindex=\"-1\"><\/a> exp_results =<\/span> tibble<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-15\" tabindex=\"-1\"><\/a> eff_size =<\/span> drawn_effect_sizes,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-16\" tabindex=\"-1\"><\/a> converted_A =<\/span> rbinom<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-17\" tabindex=\"-1\"><\/a> num_experiments, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-18\" tabindex=\"-1\"><\/a> used_sample_size, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-19\" tabindex=\"-1\"><\/a> baseline<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-20\" tabindex=\"-1\"><\/a> ),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-21\" tabindex=\"-1\"><\/a> converted_B =<\/span> rbinom<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-22\" tabindex=\"-1\"><\/a> num_experiments, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-23\" tabindex=\"-1\"><\/a> used_sample_size, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-24\" tabindex=\"-1\"><\/a> baseline +<\/span> drawn_effect_sizes<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-25\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-26\" tabindex=\"-1\"><\/a> )|><\/span> mutate<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-27\" tabindex=\"-1\"><\/a> conv_rate_A =<\/span> converted_A \/<\/span> used_sample_size,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-28\" tabindex=\"-1\"><\/a> conv_rate_B =<\/span> converted_B \/<\/span> used_sample_size,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-29\" tabindex=\"-1\"><\/a> ) |><\/span> mutate<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-30\" tabindex=\"-1\"><\/a> mean_diff =<\/span> conv_rate_B -<\/span> conv_rate_A,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-31\" tabindex=\"-1\"><\/a> st_error =<\/span> sqrt<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-32\" tabindex=\"-1\"><\/a> (conv_rate_A *<\/span> (1<\/span> -<\/span> conv_rate_A) \/<\/span> used_sample_size) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-33\" tabindex=\"-1\"><\/a> (conv_rate_B *<\/span> (1<\/span> -<\/span> conv_rate_B) \/<\/span> used_sample_size)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-34\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-35\" tabindex=\"-1\"><\/a> ) |><\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-36\" tabindex=\"-1\"><\/a> mutate<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-37\" tabindex=\"-1\"><\/a> is_stat_sig =<\/span> abs<\/span>(mean_diff \/<\/span> st_error) >=<\/span> qnorm<\/span>(0.975<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-38\" tabindex=\"-1\"><\/a> ) |><\/span> mutate<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-39\" tabindex=\"-1\"><\/a> sign_error =<\/span> if_else<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-40\" tabindex=\"-1\"><\/a> is_stat_sig, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-41\" tabindex=\"-1\"><\/a> sign<\/span>(mean_diff) !=<\/span> sign<\/span>(drawn_effect_sizes), <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-42\" tabindex=\"-1\"><\/a> NA<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-43\" tabindex=\"-1\"><\/a> ),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-44\" tabindex=\"-1\"><\/a> exaggeration =<\/span> if_else<\/span>(is_stat_sig, abs<\/span>(mean_diff -<\/span> drawn_effect_sizes), NA<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-45\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-46\" tabindex=\"-1\"><\/a> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-47\" tabindex=\"-1\"><\/a> exp_results |><\/span> summarize<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-48\" tabindex=\"-1\"><\/a> distribution =<\/span> d$<\/span>name,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-49\" tabindex=\"-1\"><\/a> detection_rate =<\/span> mean<\/span>(is_stat_sig),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-50\" tabindex=\"-1\"><\/a> avg_sign_error =<\/span> mean<\/span>(replace_na<\/span>(sign_error, F)),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-51\" tabindex=\"-1\"><\/a> avg_exaggeration =<\/span> mean<\/span>(replace_na<\/span>(exaggeration, 0<\/span>)),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-52\" tabindex=\"-1\"><\/a> avg_TypeS =<\/span> mean<\/span>(sign_error, na.rm=<\/span>T),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-53\" tabindex=\"-1\"><\/a> avg_conditional_exaggeration =<\/span> mean<\/span>(exaggeration, na.rm=<\/span>T)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-54\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-55\" tabindex=\"-1\"><\/a>}) |><\/span> bind_rows<\/span>()<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-56\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-57\" tabindex=\"-1\"><\/a>appendix_sim |><\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-58\" tabindex=\"-1\"><\/a> kbl<\/span>(caption=<\/span>'Simulation results using N = 20% power at MDE of 0.015'<\/span>, digits=<\/span>4<\/span>) |><\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-59\" tabindex=\"-1\"><\/a> kable_styling<\/span>()<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<table class=\"table table-sm table-striped small\" data-quarto-postprocess=\"true\">\n<caption>Simulation results using N = 20% power at MDE of 0.015<\/caption>\n<thead>\n<tr class=\"header\">\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: left;\">distribution<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">detection_rate<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_sign_error<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_exaggeration<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_TypeS<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_conditional_exaggeration<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td style=\"text-align: left;\">Startup team<\/td>\n<td style=\"text-align: right;\">0.5033<\/td>\n<td style=\"text-align: right;\">0.0028<\/td>\n<td style=\"text-align: right;\">0.0057<\/td>\n<td style=\"text-align: right;\">0.0056<\/td>\n<td style=\"text-align: right;\">0.0114<\/td>\n<\/tr>\n<tr class=\"even\">\n<td style=\"text-align: left;\">Mature team<\/td>\n<td style=\"text-align: right;\">0.0994<\/td>\n<td style=\"text-align: right;\">0.0125<\/td>\n<td style=\"text-align: right;\">0.0022<\/td>\n<td style=\"text-align: right;\">0.1262<\/td>\n<td style=\"text-align: right;\">0.0225<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb17\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb17-1\" tabindex=\"-1\"><\/a>lapply<\/span>(eff_size_distrs, function<\/span>(d) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-2\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-3\" tabindex=\"-1\"><\/a> distribution =<\/span> d$<\/span>name,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-4\" tabindex=\"-1\"><\/a> detection_rate =<\/span> estimate_detection_rate<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-5\" tabindex=\"-1\"><\/a> baseline =<\/span> baseline, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-6\" tabindex=\"-1\"><\/a> required_sample_size =<\/span> used_sample_size, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-7\" tabindex=\"-1\"><\/a> density_func =<\/span> d$<\/span>density<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-8\" tabindex=\"-1\"><\/a> ),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-9\" tabindex=\"-1\"><\/a> avg_sign_error =<\/span> estimate_sign_error_rate<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-10\" tabindex=\"-1\"><\/a> baseline =<\/span> baseline, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-11\" tabindex=\"-1\"><\/a> s =<\/span> used_sample_size, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-12\" tabindex=\"-1\"><\/a> density_func =<\/span> d$<\/span>density<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-13\" tabindex=\"-1\"><\/a> ),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-14\" tabindex=\"-1\"><\/a> avg_exaggeration =<\/span> estimate_abs_exaggeration<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-15\" tabindex=\"-1\"><\/a> baseline =<\/span> baseline, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-16\" tabindex=\"-1\"><\/a> s =<\/span> used_sample_size, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-17\" tabindex=\"-1\"><\/a> density_func =<\/span> d$<\/span>density<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-18\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-19\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-20\" tabindex=\"-1\"><\/a>}) |><\/span> bind_rows<\/span>() |><\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-21\" tabindex=\"-1\"><\/a> mutate<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-22\" tabindex=\"-1\"><\/a> avg_TypeS =<\/span> avg_sign_error \/<\/span> detection_rate,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-23\" tabindex=\"-1\"><\/a> avg_conditional_exaggeration =<\/span> avg_exaggeration \/<\/span> detection_rate<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-24\" tabindex=\"-1\"><\/a> ) |><\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-25\" tabindex=\"-1\"><\/a> kbl<\/span>(caption=<\/span>'Integration results with equivalent parameters'<\/span>, digits=<\/span>4<\/span>) |><\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-26\" tabindex=\"-1\"><\/a> kable_styling<\/span>()<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<table class=\"table table-sm table-striped small\" data-quarto-postprocess=\"true\">\n<caption>Integration results with equivalent parameters<\/caption>\n<thead>\n<tr class=\"header\">\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: left;\">distribution<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">detection_rate<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_sign_error<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_exaggeration<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_TypeS<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_conditional_exaggeration<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td style=\"text-align: left;\">Startup team<\/td>\n<td style=\"text-align: right;\">0.4998<\/td>\n<td style=\"text-align: right;\">0.0026<\/td>\n<td style=\"text-align: right;\">0.0029<\/td>\n<td style=\"text-align: right;\">0.0052<\/td>\n<td style=\"text-align: right;\">0.0057<\/td>\n<\/tr>\n<tr class=\"even\">\n<td style=\"text-align: left;\">Mature team<\/td>\n<td style=\"text-align: right;\">0.0864<\/td>\n<td style=\"text-align: right;\">0.0118<\/td>\n<td style=\"text-align: right;\">0.0021<\/td>\n<td style=\"text-align: right;\">0.1364<\/td>\n<td style=\"text-align: right;\">0.0247<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/section>\n<script id=\"quarto-html-after-body\" type=\"application\/javascript\">\n\nwindow.document.addEventListener(\"DOMContentLoaded\", function (event) {\n\n const toggleBodyColorMode = (bsSheetEl) => {\n\n const mode = bsSheetEl.getAttribute(\"data-mode\");\n\n const bodyEl = window.document.querySelector(\"body\");\n\n if (mode === \"dark\") {\n\n bodyEl.classList.add(\"quarto-dark\");\n\n bodyEl.classList.remove(\"quarto-light\");\n\n } else {\n\n bodyEl.classList.add(\"quarto-light\");\n\n bodyEl.classList.remove(\"quarto-dark\");\n\n }\n\n }\n\n const toggleBodyColorPrimary = () => {\n\n const 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placeholderEl = window.document.getElementById(\n\n placeholderDescriptor.id\n\n );\n\n if (placeholderEl) {\n\n placeholderEl.remove();\n\n }\n\n\n\n el.classList.remove(\"rollup\");\n\n };\n\n\n\n if (isReaderMode()) {\n\n convertToMenu();\n\n isVisible = false;\n\n } else {\n\n \/\/ Find the top and bottom o the element that is being managed\n\n const elTop = el.offsetTop;\n\n const elBottom =\n\n elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;\n\n\n\n if (!isVisible) {\n\n \/\/ If the element is current not visible reveal if there are\n\n \/\/ no conflicts with overlay regions\n\n if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {\n\n convertToSidebar();\n\n isVisible = true;\n\n }\n\n } else {\n\n \/\/ If the element is visible, hide it if it conflicts with overlay regions\n\n \/\/ and insert a placeholder toggle (or if we're in reader mode)\n\n if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {\n\n convertToMenu();\n\n isVisible = false;\n\n }\n\n }\n\n }\n\n 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for (const childEl of childNodes) {\n\n if (childEl.classList.contains(visibleCls)) {\n\n childEl.classList.remove(\"collapse\");\n\n } else {\n\n childEl.classList.add(\"collapse\");\n\n }\n\n }\n\n }\n\n }\n\n }\n\n\n\n layoutMarginEls();\n\n });\n\n }\n\n }\n\n\n\n \/\/ Manage the visibility of the toc and the sidebar\n\n const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {\n\n id: \"quarto-toc-toggle\",\n\n titleSelector: \"#toc-title\",\n\n dismissOnClick: true,\n\n });\n\n const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {\n\n id: \"quarto-sidebarnav-toggle\",\n\n titleSelector: \".title\",\n\n dismissOnClick: false,\n\n });\n\n let tocLeftScrollVisibility;\n\n if (leftTocEl) {\n\n tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {\n\n id: \"quarto-lefttoc-toggle\",\n\n titleSelector: \"#toc-title\",\n\n dismissOnClick: true,\n\n });\n\n }\n\n\n\n \/\/ Find the first element that uses formatting in special columns\n\n const conflictingEls = window.document.body.querySelectorAll(\n\n '[class^=\"column-\"], [class*=\" column-\"], aside, [class*=\"margin-caption\"], [class*=\" margin-caption\"], [class*=\"margin-ref\"], [class*=\" margin-ref\"]'\n\n );\n\n\n\n \/\/ Filter all the possibly conflicting elements into ones\n\n \/\/ the do conflict on the left or ride side\n\n const arrConflictingEls = Array.from(conflictingEls);\n\n const leftSideConflictEls = arrConflictingEls.filter((el) => {\n\n if (el.tagName === \"ASIDE\") {\n\n return false;\n\n }\n\n return Array.from(el.classList).find((className) => {\n\n return (\n\n className !== \"column-body\" &&\n\n className.startsWith(\"column-\") &&\n\n !className.endsWith(\"right\") &&\n\n !className.endsWith(\"container\") &&\n\n className !== \"column-margin\"\n\n );\n\n });\n\n });\n\n const rightSideConflictEls = arrConflictingEls.filter((el) => {\n\n if (el.tagName === \"ASIDE\") {\n\n return true;\n\n }\n\n\n\n const hasMarginCaption = 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J,Z=\"x\"===y?zt:Vt,tt=\"x\"===y?Rt:qt,et=A[w],it=\"y\"===w?\"height\":\"width\",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:[\"offset\"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)\/t.offsetWidth||1,n=we(e.height)\/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&((\"body\"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:\"bottom\",modifiers:[],strategy:\"absolute\"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&\"function\"==typeof t.getBoundingClientRect)}))}function mi(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?fi:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:\"bottom\",orderedModifiers:[],options:Object.assign({},fi,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s=\"function\"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:pe(t)?Je(t):t.contextElement?Je(t.contextElement):[],popper:Je(e)};var r,c,u=function(t){var e=ui(t);return de.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if(\"function\"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(pi(e,i)){a.rects={reference:di(e,$e(i),\"fixed\"===a.options.strategy),popper:Ce(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;\"function\"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!pi(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var gi=mi(),_i=mi({defaultModifiers:[Re,ci,Be,_e]}),bi=mi({defaultModifiers:[Re,ci,Be,_e,li,si,hi,je,ai]});const vi=Object.freeze(Object.defineProperty({__proto__:null,afterMain:ae,afterRead:se,afterWrite:he,applyStyles:_e,arrow:je,auto:Kt,basePlacements:Qt,beforeMain:oe,beforeRead:ie,beforeWrite:le,bottom:Rt,clippingParents:Ut,computeStyles:Be,createPopper:bi,createPopperBase:gi,createPopperLite:_i,detectOverflow:ii,end:Yt,eventListeners:Re,flip:si,hide:ai,left:Vt,main:re,modifierPhases:de,offset:li,placements:ee,popper:Jt,popperGenerator:mi,popperOffsets:ci,preventOverflow:hi,read:ne,reference:Zt,right:qt,start:Xt,top:zt,variationPlacements:te,viewport:Gt,write:ce},Symbol.toStringTag,{value:\"Module\"})),yi=\"dropdown\",wi=\".bs.dropdown\",Ai=\".data-api\",Ei=\"ArrowUp\",Ti=\"ArrowDown\",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ai}`,Si=`keydown${wi}${Ai}`,Di=`keyup${wi}${Ai}`,$i=\"show\",Ii='[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=\".dropdown-menu\",Mi=p()?\"top-end\":\"top-start\",ji=p()?\"top-start\":\"top-end\",Fi=p()?\"bottom-end\":\"bottom-start\",Hi=p()?\"bottom-start\":\"bottom-end\",Wi=p()?\"left-start\":\"right-start\",Bi=p()?\"right-start\":\"left-start\",zi={autoClose:!0,boundary:\"clippingParents\",display:\"dynamic\",offset:[0,2],popperConfig:null,reference:\"toggle\"},Ri={autoClose:\"(boolean|string)\",boundary:\"(string|element)\",display:\"string\",offset:\"(array|string|function)\",popperConfig:\"(null|object|function)\",reference:\"(string|element|object)\"};class qi extends W{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=z.next(this._element,Pi)[0]||z.prev(this._element,Pi)[0]||z.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!N.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),\"ontouchstart\"in document.documentElement&&!this._parent.closest(\".navbar-nav\"))for(const t of[].concat(...document.body.children))N.on(t,\"mouseover\",h);this._element.focus(),this._element.setAttribute(\"aria-expanded\",!0),this._menu.classList.add($i),this._element.classList.add($i),N.trigger(this._element,ki,t)}}hide(){if(l(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!N.trigger(this._element,Ci,t).defaultPrevented){if(\"ontouchstart\"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,\"mouseover\",h);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute(\"aria-expanded\",\"false\"),F.removeDataAttribute(this._menu,\"popper\"),N.trigger(this._element,Oi,t)}}_getConfig(t){if(\"object\"==typeof(t=super._getConfig(t)).reference&&!o(t.reference)&&\"function\"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);return t}_createPopper(){if(void 0===vi)throw new TypeError(\"Bootstrap's dropdowns require Popper (https:\/\/popper.js.org)\");let t=this._element;\"parent\"===this._config.reference?t=this._parent:o(this._config.reference)?t=r(this._config.reference):\"object\"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=bi(t,this._menu,e)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains(\"dropend\"))return Wi;if(t.classList.contains(\"dropstart\"))return Bi;if(t.classList.contains(\"dropup-center\"))return\"top\";if(t.classList.contains(\"dropdown-center\"))return\"bottom\";const e=\"end\"===getComputedStyle(this._menu).getPropertyValue(\"--bs-position\").trim();return t.classList.contains(\"dropup\")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(\".navbar\")}_getOffset(){const{offset:t}=this._config;return\"string\"==typeof t?t.split(\",\").map((t=>Number.parseInt(t,10))):\"function\"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:\"preventOverflow\",options:{boundary:this._config.boundary}},{name:\"offset\",options:{offset:this._getOffset()}}]};return(this._inNavbar||\"static\"===this._config.display)&&(F.setDataAttribute(this._menu,\"popper\",\"static\"),t.modifiers=[{name:\"applyStyles\",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(\".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)\",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if(\"string\"==typeof t){if(void 0===e[t])throw new TypeError(`No method named \"${t}\"`);e[t]()}}))}static clearMenus(t){if(2===t.button||\"keyup\"===t.type&&\"Tab\"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||\"inside\"===e._config.autoClose&&!s||\"outside\"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&(\"keyup\"===t.type&&\"Tab\"===t.key||\/input|select|option|textarea|form\/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};\"click\"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=\/input|textarea\/i.test(t.target.tagName),i=\"Escape\"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi=\"backdrop\",Ki=\"show\",Qi=`mousedown.bs.${Vi}`,Xi={className:\"modal-backdrop\",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:\"body\"},Yi={className:\"string\",clickCallback:\"(function|null)\",isAnimated:\"boolean\",isVisible:\"boolean\",rootElement:\"(element|string)\"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement(\"div\");t.className=this._config.className,this._config.isAnimated&&t.classList.add(\"fade\"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=\".bs.focustrap\",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn=\"backward\",en={autofocus:!0,trapElement:null},nn={autofocus:\"boolean\",trapElement:\"element\"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return\"focustrap\"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){\"Tab\"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:\"forward\")}}const on=\".fixed-top, .fixed-bottom, .is-fixed, .sticky-top\",rn=\".sticky-top\",an=\"padding-right\",ln=\"margin-right\";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,\"overflow\"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,\"overflow\"),this._element.style.overflow=\"hidden\"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=\".bs.modal\",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn=\"modal-open\",An=\"show\",En=\"modal-static\",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:\"(boolean|string)\",focus:\"boolean\",keyboard:\"boolean\"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(\".modal-dialog\",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return\"modal\"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display=\"block\",this._element.removeAttribute(\"aria-hidden\"),this._element.setAttribute(\"aria-modal\",!0),this._element.setAttribute(\"role\",\"dialog\"),this._element.scrollTop=0;const e=z.findOne(\".modal-body\",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{\"Escape\"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&(\"static\"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display=\"none\",this._element.setAttribute(\"aria-hidden\",!0),this._element.removeAttribute(\"aria-modal\"),this._element.removeAttribute(\"role\"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains(\"fade\")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;\"hidden\"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY=\"hidden\"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?\"paddingLeft\":\"paddingRight\";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?\"paddingRight\":\"paddingLeft\";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft=\"\",this._element.style.paddingRight=\"\"}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if(\"string\"==typeof t){if(void 0===i[t])throw new TypeError(`No method named \"${t}\"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle=\"modal\"]',(function(t){const e=z.getElementFromSelector(this);[\"A\",\"AREA\"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(\".modal.show\");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=\".bs.offcanvas\",kn=\".data-api\",Ln=`load${xn}${kn}`,Sn=\"show\",Dn=\"showing\",$n=\"hiding\",In=\".offcanvas.show\",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:\"(boolean|string)\",keyboard:\"boolean\",scroll:\"boolean\"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return\"offcanvas\"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute(\"aria-modal\",!0),this._element.setAttribute(\"role\",\"dialog\"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute(\"aria-modal\"),this._element.removeAttribute(\"role\"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:\"offcanvas-backdrop\",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{\"static\"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{\"Escape\"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if(\"string\"==typeof t){if(void 0===e[t]||t.startsWith(\"_\")||\"constructor\"===t)throw new TypeError(`No method named \"${t}\"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle=\"offcanvas\"]',(function(t){const e=z.getElementFromSelector(this);if([\"A\",\"AREA\"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find(\"[aria-modal][class*=show][class*=offcanvas-]\"))\"fixed\"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={\"*\":[\"class\",\"dir\",\"id\",\"lang\",\"role\",\/^aria-[\\w-]*$\/i],a:[\"target\",\"href\",\"title\",\"rel\"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:[\"src\",\"srcset\",\"alt\",\"title\",\"width\",\"height\"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set([\"background\",\"cite\",\"href\",\"itemtype\",\"longdesc\",\"poster\",\"src\",\"xlink:href\"]),Qn=\/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:\/?#]*(?:[\/?#]|$))\/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:\"\",html:!1,sanitize:!0,sanitizeFn:null,template:\"<div><\/div>\"},Un={allowList:\"object\",content:\"object\",extraClass:\"(string|function)\",html:\"boolean\",sanitize:\"boolean\",sanitizeFn:\"(null|function)\",template:\"string\"},Gn={entry:\"(string|element|function|null)\",selector:\"(string|element)\"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return\"TemplateFactory\"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement(\"div\");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(\" \")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&\"function\"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,\"text\/html\"),s=[].concat(...n.body.querySelectorAll(\"*\"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e[\"*\"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML=\"\",void e.append(t);e.textContent=t.textContent}}const Zn=new Set([\"sanitize\",\"allowList\",\"sanitizeFn\"]),ts=\"fade\",es=\"show\",is=\".modal\",ns=\"hide.bs.modal\",ss=\"hover\",os=\"focus\",rs={AUTO:\"auto\",TOP:\"top\",RIGHT:p()?\"left\":\"right\",BOTTOM:\"bottom\",LEFT:p()?\"right\":\"left\"},as={allowList:Vn,animation:!0,boundary:\"clippingParents\",container:!1,customClass:\"\",delay:0,fallbackPlacements:[\"top\",\"right\",\"bottom\",\"left\"],html:!1,offset:[0,6],placement:\"top\",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class=\"tooltip\" role=\"tooltip\"><div class=\"tooltip-arrow\"><\/div><div class=\"tooltip-inner\"><\/div><\/div>',title:\"\",trigger:\"hover focus\"},ls={allowList:\"object\",animation:\"boolean\",boundary:\"(string|element)\",container:\"(string|element|boolean)\",customClass:\"(string|function)\",delay:\"(number|object)\",fallbackPlacements:\"array\",html:\"boolean\",offset:\"(array|string|function)\",placement:\"(string|function)\",popperConfig:\"(null|object|function)\",sanitize:\"boolean\",sanitizeFn:\"(null|function)\",selector:\"(string|boolean)\",template:\"string\",title:\"(string|element|function)\",trigger:\"string\"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError(\"Bootstrap's tooltips require Popper (https:\/\/popper.js.org)\");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return\"tooltip\"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute(\"data-bs-original-title\")&&this._element.setAttribute(\"title\",this._element.getAttribute(\"data-bs-original-title\")),this._disposePopper(),super.dispose()}show(){if(\"none\"===this._element.style.display)throw new Error(\"Please use show on visible elements\");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName(\"show\")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute(\"aria-describedby\",i.getAttribute(\"id\"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName(\"inserted\"))),this._popper=this._createPopper(i),i.classList.add(es),\"ontouchstart\"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,\"mouseover\",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName(\"shown\")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName(\"hide\")).defaultPrevented){if(this._getTipElement().classList.remove(es),\"ontouchstart\"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,\"mouseover\",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute(\"aria-describedby\"),N.trigger(this._element,this.constructor.eventName(\"hidden\")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute(\"id\",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{\".tooltip-inner\":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute(\"data-bs-original-title\")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return\"string\"==typeof t?t.split(\",\").map((t=>Number.parseInt(t,10))):\"function\"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:\"flip\",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:\"offset\",options:{offset:this._getOffset()}},{name:\"preventOverflow\",options:{boundary:this._config.boundary}},{name:\"arrow\",options:{element:`.${this.constructor.NAME}-arrow`}},{name:\"preSetPlacement\",enabled:!0,phase:\"beforeMain\",fn:t=>{this._getTipElement().setAttribute(\"data-popper-placement\",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(\" \");for(const e of t)if(\"click\"===e)N.on(this._element,this.constructor.eventName(\"click\"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if(\"manual\"!==e){const t=e===ss?this.constructor.eventName(\"mouseenter\"):this.constructor.eventName(\"focusin\"),i=e===ss?this.constructor.eventName(\"mouseleave\"):this.constructor.eventName(\"focusout\");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger[\"focusin\"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger[\"focusout\"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute(\"title\");t&&(this._element.getAttribute(\"aria-label\")||this._element.textContent.trim()||this._element.setAttribute(\"aria-label\",t),this._element.setAttribute(\"data-bs-original-title\",t),this._element.removeAttribute(\"title\"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,...\"object\"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),\"number\"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),\"number\"==typeof t.title&&(t.title=t.title.toString()),\"number\"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger=\"manual\",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if(\"string\"==typeof t){if(void 0===e[t])throw new TypeError(`No method named \"${t}\"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:\"\",offset:[0,8],placement:\"right\",template:'<div class=\"popover\" role=\"tooltip\"><div class=\"popover-arrow\"><\/div><h3 class=\"popover-header\"><\/h3><div class=\"popover-body\"><\/div><\/div>',trigger:\"click\"},ds={...cs.DefaultType,content:\"(null|string|element|function)\"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return\"popover\"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{\".popover-header\":this._getTitle(),\".popover-body\":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if(\"string\"==typeof t){if(void 0===e[t])throw new TypeError(`No method named \"${t}\"`);e[t]()}}))}}m(us);const fs=\".bs.scrollspy\",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s=\"active\",bs=\"[href]\",vs=\".nav-link\",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:\"0px 0px -25%\",smoothScroll:!1,target:null,threshold:[.1,.5,1]},As={offset:\"(number|null)\",rootMargin:\"string\",smoothScroll:\"boolean\",target:\"element\",threshold:\"array\"};class Es extends W{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement=\"visible\"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return ws}static get DefaultType(){return As}static get NAME(){return\"scrollspy\"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=r(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,\"string\"==typeof t.threshold&&(t.threshold=t.threshold.split(\",\").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(N.off(this._config.target,ms),N.on(this._config.target,ms,bs,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:\"smooth\"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains(\"dropdown-item\"))z.findOne(\".dropdown-toggle\",t.closest(\".dropdown\")).classList.add(_s);else for(const e of z.parents(t,\".nav, .list-group\"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if(\"string\"==typeof t){if(void 0===e[t]||t.startsWith(\"_\")||\"constructor\"===t)throw new TypeError(`No method named \"${t}\"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy=\"scroll\"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=\".bs.tab\",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s=\"ArrowLeft\",Is=\"ArrowRight\",Ns=\"ArrowUp\",Ps=\"ArrowDown\",Ms=\"Home\",js=\"End\",Fs=\"active\",Hs=\"fade\",Ws=\"show\",Bs=\":not(.dropdown-toggle)\",zs='[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role=\"tab\"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle=\"tab\"], .${Fs}[data-bs-toggle=\"pill\"], .${Fs}[data-bs-toggle=\"list\"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role=\"tablist\"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return\"tab\"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{\"tab\"===t.getAttribute(\"role\")?(t.removeAttribute(\"tabindex\"),t.setAttribute(\"aria-selected\",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{\"tab\"===t.getAttribute(\"role\")?(t.setAttribute(\"aria-selected\",!1),t.setAttribute(\"tabindex\",\"-1\"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=b(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return z.find(Rs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,\"role\",\"tablist\");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute(\"aria-selected\",e),i!==t&&this._setAttributeIfNotExists(i,\"role\",\"presentation\"),e||t.setAttribute(\"tabindex\",\"-1\"),this._setAttributeIfNotExists(t,\"role\",\"tab\"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const 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id=\"quarto-bootstrap\" rel=\"stylesheet\"\/>\n<script>$(document).ready(function(){\n\n if (typeof $('[data-toggle=\"tooltip\"]').tooltip === 'function') {\n\n $('[data-toggle=\"tooltip\"]').tooltip();\n\n }\n\n if ($('[data-toggle=\"popover\"]').popover === 'function') {\n\n $('[data-toggle=\"popover\"]').popover();\n\n }\n\n});\n\n<\/script>\n\n<\/head>\n<body class=\"fullcontent\">\n<div class=\"page-layout-custom\">\n<header class=\"quarto-title-block default\" id=\"title-block-header\">\n<div class=\"quarto-title\">\n\nWhy a team that has good (but not great) ideas 75% of the time may see a win rate of just 25%, and why focusing on MDE\/statistical power alone may not always be the best<\/p>\n<\/div>\n<div class=\"quarto-title-meta\">\n<\/div>\n<\/header>\n<img decoding=\"async\" class=\"img-fluid\" 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\"\/><\/p>\nClassical statistical guarantees in A\/B testing (Type I and II error rates) are great at quantifying the reproducibility of a single experiment. As a reminder:<\/p>\n<ul>\n<li>Type I error rate, <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b1<\/mi><annotation encoding=\"application\/x-tex\">\\alpha<\/annotation><\/semantics><\/math>, guarantees that when the null hypothesis is true (typically: no difference between variants), you will only observe statistically significant results (i.e., observed data says there\u2019s a difference) <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b1<\/mi><annotation encoding=\"application\/x-tex\">\\alpha<\/annotation><\/semantics><\/math> fraction of the time. It\u2019s the false positive rate for an experiment with no effect.<\/p><\/li>\n<li>Type II error rate, <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mn>1<\/mn><mo>\u2212<\/mo><mi>\u03b2<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">1-\\beta<\/annotation><\/semantics><\/math>, guarantees that when the null hypothesis is false, you will observe data that will yield statistically significant results <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b2<\/mi><annotation encoding=\"application\/x-tex\">\\beta<\/annotation><\/semantics><\/math> fraction of the time (a.k.a statistical power), conditional on sample size, effect size of interest, and chosen <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b1<\/mi><annotation encoding=\"application\/x-tex\">\\alpha<\/annotation><\/semantics><\/math> rate. It\u2019s the false negative rate for an experiment with an underlying effect.<\/p><\/li>\n<\/ul>\nHowever, they tell us little about the long-term dynamics of an \u201cexperimentation program\u201d ( a series of experiments, each estimating the impact of a different treatment). You can\u2019t immediately answer questions such as \u201cWhat % of experiments can I expect to detect a statistically significant result in?\u201d.<\/p>\nThat, however, may be an interesting question to answer. Suppose you\u2019re Head of Data Something<\/em> in a company that ran 100 experiments last year, of which only 5% yielded positive statistically significant results (a bit below <a href=\"https:\/\/hdsr.mitpress.mit.edu\/pub\/aj31wj81\/release\/1\">an oft-quoted figure in the industry<\/a>). You may be wondering if that\u2019s normal. Is it really so that 95% of product changes had zero impact? Perhaps they weren\u2019t exactly zero, but simply smaller than you could declare as statistically significant with the sample sizes at hand?<\/p>\nIf you work in a large company, you may find yourself at the opposite of \u201cstatistically but not practically significant.\u201d After all, a conversion rate improvement of 0.5 percentage point may still mean many $$<\/em> annually. However, achieving 80% power on such an effect size requires a 3-4x sample size compared to an effect size of 1 percentage point. Are you willing to make that trade-off and make all experiments 3-4x slower?<\/p>\nUnderstanding the relationship between experiment-level design parameters (minimum detectable effect (MDE), sample size, power) and overall experimentation program win\/detection\/error rates can be pretty illuminating. So, let\u2019s figure out how to do it.<\/p>\nThis blog post is accompanied by an interactive <a href=\"https:\/\/ab-testing-long-term-error-rates.streamlit.app\/\">Streamlit app<\/a> where you can explore how all the different parameters come to life yourself.<\/p>\n<section class=\"level2\" id=\"from-statistical-power-to-detectionwin-rates\">\n<h2 data-anchor-id=\"from-statistical-power-to-detectionwin-rates\">From statistical power to detection\/win rates<\/h2>\nYou may wonder why the answer to long-term detection rates isn\u2019t simply the Type II error rate or \u201cWe will detect (i.e., declare them as statistically significant) differences when they exist 80% of the time\u201d.<\/p>\nThat\u2019s because the Type II error rate is conditional on the MDE. Assuming you use the same MDE all the time, you can claim that when the actual effect is at least as large as the MDE, we will detect it at least 80% of the time.<\/p>\nBut what if it\u2019s not? And how exactly does one think about the \u201cat least\u201d part of the statement? What if actual effects are larger\/smaller than MDE? Your power will be exactly 80% only if you always choose MDE precisely equal to the actual unobservable difference for that specific experiment. If you can do that, you don\u2019t need to experiment!<\/p>\nFrequentist approaches tend to develop tools that find upper\/lower bounds of a measure of interest, and statistical power is an excellent example of that (I saw this clearly articulated in an <a href=\"#0\">essay by Jacob Steinhardt titled \u201cBeyond Bayesians and Frequentists\u201d<\/a> - I highly recommend it).<\/p>\nHowever, we are after an expectation\u2014we want to know the average expected detection rate. For that, we\u2019ll borrow an idea from Bayesian statistics that tends to be better suited for such purposes. Instead of working with fixed values, we will assume a distribution of likely effect sizes (a.k.a., a prior distribution) and then integrate over that distribution to get expected rates.<\/p>\nTo be precise, by integrating over a distribution of hypothesized treatment effect sizes <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>A<\/mi><annotation encoding=\"application\/x-tex\">A<\/annotation><\/semantics><\/math> (we\u2019ll call the distribution <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><msub><mi>F<\/mi><mi>A<\/mi><\/msub><annotation encoding=\"application\/x-tex\">F_A<\/annotation><\/semantics><\/math>), we can calculate:<\/p>\n<ul>\n<li>Expected \u201cdetection rate\u201d - the % of experiments we would declare statistically significant given they have a non-zero actual treatment effect.<\/p><\/li>\n<li>Expected \u201cwin rate\u201d - the % of experiments we would declare statistically significant, but we count only experiments with a positive treatment effect.<\/p><\/li>\n<\/ul>\nMathematically:<\/p>\n<math display=\"block\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mtext mathvariant=\"normal\">expected detection rate<\/mtext><mo>=<\/mo><msubsup><mo>\u222b<\/mo><mrow><mo>\u2212<\/mo><mi>\u221e<\/mi><\/mrow><mrow><mo>+<\/mo><mi>\u221e<\/mi><\/mrow><\/msubsup><msub><mi>F<\/mi><mi>A<\/mi><\/msub><mrow><mo form=\"prefix\" stretchy=\"true\">(<\/mo><mi>x<\/mi><mo form=\"postfix\" stretchy=\"true\">)<\/mo><\/mrow><mtext mathvariant=\"normal\">Pr<\/mtext><mrow><mo form=\"prefix\" stretchy=\"true\">(<\/mo><mrow><mtext mathvariant=\"normal\">reject <\/mtext><mspace width=\"0.333em\"><\/mspace><\/mrow><msub><mi>H<\/mi><mn>0<\/mn><\/msub><mo form=\"prefix\" stretchy=\"false\">|<\/mo><mi>x<\/mi><mo>\u2260<\/mo><mn>0<\/mn><mo>,<\/mo><mi>\u03b1<\/mi><mo>,<\/mo><mi>n<\/mi><mo>,<\/mo><msup><mi>\u03c3<\/mi><mn>2<\/mn><\/msup><mo form=\"postfix\" stretchy=\"true\">)<\/mo><\/mrow><mi>d<\/mi><mi>x<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">\n\n\\text{expected detection rate} = \\int_{-\\infty}^{+\\infty} F_A(x) \\text{Pr}(\\text{reject } H_0 | x \\neq 0, \\alpha, n, \\sigma^2) dx\n\n<\/annotation><\/semantics><\/math> <math display=\"block\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mtext mathvariant=\"normal\">expected win rate<\/mtext><mo>=<\/mo><msubsup><mo>\u222b<\/mo><mn>0<\/mn><mrow><mo>+<\/mo><mi>\u221e<\/mi><\/mrow><\/msubsup><msub><mi>F<\/mi><mi>A<\/mi><\/msub><mrow><mo form=\"prefix\" stretchy=\"true\">(<\/mo><mi>x<\/mi><mo form=\"postfix\" stretchy=\"true\">)<\/mo><\/mrow><mtext mathvariant=\"normal\">Pr<\/mtext><mrow><mo form=\"prefix\" stretchy=\"true\">(<\/mo><mrow><mtext mathvariant=\"normal\">reject <\/mtext><mspace width=\"0.333em\"><\/mspace><\/mrow><msub><mi>H<\/mi><mn>0<\/mn><\/msub><mo form=\"prefix\" stretchy=\"false\">|<\/mo><mi>x<\/mi><mo>\u2260<\/mo><mn>0<\/mn><mo>,<\/mo><mi>n<\/mi><mo>,<\/mo><mi>\u03b1<\/mi><mo>,<\/mo><msup><mi>\u03c3<\/mi><mn>2<\/mn><\/msup><mo form=\"postfix\" stretchy=\"true\">)<\/mo><\/mrow><mi>d<\/mi><mi>x<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">\n\n\\text{expected win rate} = \\int_0^{+\\infty} F_A(x) \\text{Pr}(\\text{reject } H_0 | x \\neq 0, n, \\alpha, \\sigma^2) dx\n\n<\/annotation><\/semantics><\/math><\/p>\nwhere <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>n<\/mi><annotation encoding=\"application\/x-tex\">n<\/annotation><\/semantics><\/math> is the sample size, <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><msup><mi>\u03c3<\/mi><mn>2<\/mn><\/msup><annotation encoding=\"application\/x-tex\">\\sigma^2<\/annotation><\/semantics><\/math> is the variance of the outcome metric, and <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b1<\/mi><annotation encoding=\"application\/x-tex\">\\alpha<\/annotation><\/semantics><\/math> is the chosen statistical significance level.<\/p>\nOne immediate thing to note is that detection\/win rates do not depend on <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>M<\/mi><mi>D<\/mi><mi>E<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">MDE<\/annotation><\/semantics><\/math> or <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b2<\/mi><annotation encoding=\"application\/x-tex\">\\beta<\/annotation><\/semantics><\/math> (statistical power). The only levers you have to increase them are larger samples, variance reduction techniques (e.g., by using covariates, such as CUPED-like methods), better treatments (changes in <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><msub><mi>F<\/mi><mi>A<\/mi><\/msub><annotation encoding=\"application\/x-tex\">F_A<\/annotation><\/semantics><\/math>), and different <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b1<\/mi><annotation encoding=\"application\/x-tex\">\\alpha<\/annotation><\/semantics><\/math> levels.<\/p>\nIf your immediate reaction is, \u201cOMG, a subjective assumption on effect size distribution (gasp!)\u201d, I would like to remind you that choosing the MDE threshold for power calculations is subjective, too. Ideally, one would pick an MDE upfront and tune the sample size to achieve the desired statistical power. However, in practice, <a href=\"https:\/\/aurimas.eu\/blog\/2023\/02\/getting-faster-to-decisions-in-a-b-tests-part-2-misinterpretations-and-practical-challenges-of-classical-hypothesis-testing\/\">there\u2019s always an incentive to cherry-pick an MDE itself<\/a>, given a fixed sample size. It looks better to report a power of 80% with an MDE of 0.015 than a power of 50% with an MDE of 0.01.<\/p>\nHaving said that, if you use MDE correctly and report statistical power together with the MDE, bad MDE choices are more transparent than flawed effect size distribution assumptions. But if we want to estimate average detection\/win rates, that\u2019s the cost we need to pay - there\u2019s no free lunch!<\/p>\n<section class=\"level3\" id=\"selecting-an-effect-size-distribution\">\n<h3 data-anchor-id=\"selecting-an-effect-size-distribution\">Selecting an effect size distribution<\/h3>\nSo, how do we select a potential effect size distribution?<\/p>\n<ul>\n<li>Historical experiment results could be a good starting point for understanding where most of the distribution density should be. If most experiments see effect sizes of a few percentage points, then the distribution should reflect that, too. Importantly, we should consider observed treatment effects in all experiments, not just the statistically significant ones.<\/p><\/li>\n<li>Teams have domain expertise and are paid to do their work, so we\u2019d hope they do better than a random flip of a coin - the mean\/median of the distribution should be positive.<\/p><\/li>\n<li>The distribution should not be symmetric. Teams may occasionally experiment with more drastic changes, but safeguards (dogfooding, small-scale pilots, UX research) should prevent ideas with a major negative impact from reaching the experimentation phase.<\/p><\/li>\n<\/ul>\nFrom a modeling perspective, a (shifted\/scaled) Gamma distribution is a good option for modeling such skewed distributions with limited downside and a longer tail of positive effects.<\/p>\nFor the sake of this blog post, I will use two distributions representing different hypothetical teams:<\/p>\n<ul>\n<li>Mature product team<\/strong>. This team manages a mature product with a well-established user base. Experiments usually represent incremental changes and minor features, and thus, an average experiment achieves just a <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mn>0.5<\/mn><annotation encoding=\"application\/x-tex\">0.5<\/annotation><\/semantics><\/math> percentage point improvement. They extensively rely on dogfooding, UXR, and other means to test ideas before they reach the experimentation phase, thus protecting the downside very well. We\u2019ll model it with a Gamma distribution.<\/p><\/li>\n<li>Startup product team.<\/strong> This team is still in the product-market fit stage. They tend to make wide-ranging changes and like moving fast, without too much research before the experimentation phase. Because it\u2019s a less polished product, experiments tend to produce higher gains (<math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mn>2.0<\/mn><annotation encoding=\"application\/x-tex\">2.0<\/annotation><\/semantics><\/math> percentage point improvements, on average), but they also see negative impacts from changes tested more often. We\u2019ll model it with a Normal distribution.<\/p><\/li>\n<\/ul>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb1\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb1-1\" tabindex=\"-1\"><\/a>eff_size_distrs =<\/span> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-2\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-3\" tabindex=\"-1\"><\/a> name =<\/span> 'Startup team'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-4\" tabindex=\"-1\"><\/a> distr =<\/span> 'x ~ N(0.02, 0.03)'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-5\" tabindex=\"-1\"><\/a> generator =<\/span> function<\/span>() rnorm<\/span>(1<\/span>, mean=<\/span>0.02<\/span>, sd=<\/span>0.03<\/span>),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-6\" tabindex=\"-1\"><\/a> density =<\/span> function<\/span>(x) dnorm<\/span>(x, mean =<\/span> 0.02<\/span>, sd =<\/span> 0.03<\/span>) <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-7\" tabindex=\"-1\"><\/a> ),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-8\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-9\" tabindex=\"-1\"><\/a> name =<\/span> 'Mature team'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-10\" tabindex=\"-1\"><\/a> distr =<\/span> 'x ~ Gamma(2,200)) - 0.005'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-11\" tabindex=\"-1\"><\/a> generator =<\/span> function<\/span>() rgamma<\/span>(1<\/span>, shape=<\/span>2<\/span>, rate =<\/span> 200<\/span>) -<\/span> 0.005<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-12\" tabindex=\"-1\"><\/a> density =<\/span> function<\/span>(x) dgamma<\/span>(x +<\/span> 0.005<\/span>, shape=<\/span>2<\/span>, rate =<\/span> 200<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-13\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb1-14\" tabindex=\"-1\"><\/a>)<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<\/div>\nHere\u2019s how that looks visually, with some summary statistics below.<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb2\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb2-1\" tabindex=\"-1\"><\/a>emp =<\/span> lapply<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-2\" tabindex=\"-1\"><\/a> eff_size_distrs,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-3\" tabindex=\"-1\"><\/a> function<\/span>(d) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-4\" tabindex=\"-1\"><\/a> r =<\/span> list<\/span>()<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-5\" tabindex=\"-1\"><\/a> r[[d$<\/span>name]] =<\/span> sapply<\/span>(1<\/span>:<\/span>50000<\/span>, function<\/span>(i) d$<\/span>generator<\/span>())<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-6\" tabindex=\"-1\"><\/a> r<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-7\" tabindex=\"-1\"><\/a> }<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-8\" tabindex=\"-1\"><\/a>) |><\/span> bind_cols<\/span>() |><\/span> pivot_longer<\/span>(everything<\/span>(), names_to =<\/span> \"Assumed distribution\"<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-9\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-10\" tabindex=\"-1\"><\/a>ggplot<\/span>(emp) +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-11\" tabindex=\"-1\"><\/a> #geom_density(aes(x=value, color=`Assumed distribution`)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-12\" tabindex=\"-1\"><\/a> sapply<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-13\" tabindex=\"-1\"><\/a> eff_size_distrs, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-14\" tabindex=\"-1\"><\/a> function<\/span>(d) stat_function<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-15\" tabindex=\"-1\"><\/a> aes<\/span>(color=<\/span>d$<\/span>name, fill=<\/span>d$<\/span>name), <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-16\" tabindex=\"-1\"><\/a> fun =<\/span> d$<\/span>density,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-17\" tabindex=\"-1\"><\/a> data =<\/span> tibble<\/span>(name =<\/span> d$<\/span>name, full_name =<\/span> paste<\/span>(d$<\/span>name, ''<\/span>, d$<\/span>distr)),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-18\" tabindex=\"-1\"><\/a> geom=<\/span>\"area\"<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-19\" tabindex=\"-1\"><\/a> alpha=<\/span>0.5<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-20\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-21\" tabindex=\"-1\"><\/a>) +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-22\" tabindex=\"-1\"><\/a> xlim<\/span>(-<\/span>0.05<\/span>, 0.1<\/span>) +<\/span> labs<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-23\" tabindex=\"-1\"><\/a> y=<\/span>'Density'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-24\" tabindex=\"-1\"><\/a> color=<\/span>'Assumed effect size distribution'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-25\" tabindex=\"-1\"><\/a> x=<\/span>'Effect size'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-26\" tabindex=\"-1\"><\/a> title=<\/span>'Some possible effect size assumptions'<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-27\" tabindex=\"-1\"><\/a> ) +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-28\" tabindex=\"-1\"><\/a> theme_light<\/span>() +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-29\" tabindex=\"-1\"><\/a> theme<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-30\" tabindex=\"-1\"><\/a> panel.grid.major =<\/span> element_blank<\/span>(),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-31\" tabindex=\"-1\"><\/a> legend.position =<\/span> \"none\"<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-32\" tabindex=\"-1\"><\/a> ) +<\/span> facet_wrap<\/span>(~<\/span>full_name) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb2-33\" tabindex=\"-1\"><\/a> geom_vline<\/span>(xintercept =<\/span> 0<\/span>, linetype =<\/span> 'dotted'<\/span>)<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<div>\n<figure class=\"figure\">\n<img decoding=\"async\" class=\"img-fluid figure-img\" 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width=\"768\"\/><\/p>\n<\/figure>\n<\/div>\n<\/div>\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb3\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb3-1\" tabindex=\"-1\"><\/a>emp |><\/span> group_by<\/span>(`<\/span>Assumed distribution<\/span>`<\/span>) |><\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-2\" tabindex=\"-1\"><\/a> summarize<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-3\" tabindex=\"-1\"><\/a> `<\/span>Avg. effect<\/span>`<\/span> =<\/span> mean<\/span>(value),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-4\" tabindex=\"-1\"><\/a> `<\/span>10th pctile<\/span>`<\/span> =<\/span> quantile<\/span>(value, 0.1<\/span>),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-5\" tabindex=\"-1\"><\/a> `<\/span>Q50 (median)<\/span>`<\/span> =<\/span> quantile<\/span>(value, 0.5<\/span>),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-6\" tabindex=\"-1\"><\/a> `<\/span>90th pctile<\/span>`<\/span> =<\/span> quantile<\/span>(value, 0.9<\/span>),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-7\" tabindex=\"-1\"><\/a> `<\/span>Share of experiments with positive effect<\/span>`<\/span> =<\/span> mean<\/span>(if_else<\/span>(value ><\/span> 0<\/span>,1<\/span>,0<\/span>))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-8\" tabindex=\"-1\"><\/a> ) |><\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-9\" tabindex=\"-1\"><\/a> arrange<\/span>(-<\/span>`<\/span>Avg. effect<\/span>`<\/span>) |><\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-10\" tabindex=\"-1\"><\/a> kbl<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-11\" tabindex=\"-1\"><\/a> caption =<\/span> 'Summary statistics of assumed effect size distributions'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-12\" tabindex=\"-1\"><\/a> digits=<\/span>4<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-13\" tabindex=\"-1\"><\/a> ) |><\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb3-14\" tabindex=\"-1\"><\/a> kable_styling<\/span>()<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<table class=\"table table-sm table-striped small\" data-quarto-postprocess=\"true\">\n<caption>Summary statistics of assumed effect size distributions<\/caption>\n<thead>\n<tr class=\"header\">\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: left;\">Assumed distribution<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">Avg. effect<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">10th pctile<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">Q50 (median)<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">90th pctile<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">Share of experiments with positive effect<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td style=\"text-align: left;\">Startup team<\/td>\n<td style=\"text-align: right;\">0.0201<\/td>\n<td style=\"text-align: right;\">-0.0180<\/td>\n<td style=\"text-align: right;\">0.0199<\/td>\n<td style=\"text-align: right;\">0.0584<\/td>\n<td style=\"text-align: right;\">0.7505<\/td>\n<\/tr>\n<tr class=\"even\">\n<td style=\"text-align: left;\">Mature team<\/td>\n<td style=\"text-align: right;\">0.0050<\/td>\n<td style=\"text-align: right;\">-0.0023<\/td>\n<td style=\"text-align: right;\">0.0034<\/td>\n<td style=\"text-align: right;\">0.0143<\/td>\n<td style=\"text-align: right;\">0.7366<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\nNote that both teams are expected to produce nearly the same (~75%) share of experiments that have a positive impact. We could interpret that as both teams having a similar \u201cquality of ideas.\u201d It\u2019s just that the effect sizes differ due to the different contexts in which they operate - a median experiment of the mature team is assumed to produce just a 0.3ppt effect, while the startup team - 2ppt, a 6x difference.<\/p>\n<\/section>\n<section class=\"level3\" id=\"estimating-average-expected-detectionwin-rates\">\n<h3 data-anchor-id=\"estimating-average-expected-detectionwin-rates\">Estimating average expected detection\/win rates<\/h3>\nTo estimate detection\/win rates, we need a few more inputs. Let\u2019s assume that:<\/p>\n<ul>\n<li>Both teams are optimizing a metric that has a baseline of <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mn>0.17<\/mn><annotation encoding=\"application\/x-tex\">0.17<\/annotation><\/semantics><\/math><\/p><\/li>\n<li>Both teams run experiments with 10,000 observations per group (let\u2019s say they both used <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mn>0.015<\/mn><annotation encoding=\"application\/x-tex\">0.015<\/annotation><\/semantics><\/math> as their MDE and chose <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>\u03b2<\/mi><mo>=<\/mo><mn>0.2<\/mn><\/mrow><annotation encoding=\"application\/x-tex\">\\beta=0.2<\/annotation><\/semantics><\/math>; if you were to plug these inputs into a sample size calculation, you\u2019d get close to 10,000).<\/p><\/li>\n<\/ul>\n<div class=\"cell\">\n<details class=\"code-fold\" open=\"\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb4\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb4-1\" tabindex=\"-1\"><\/a>baseline =<\/span> 0.17<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb4-2\" tabindex=\"-1\"><\/a>required_sample_size =<\/span> 10000<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb4-3\" tabindex=\"-1\"><\/a>mde =<\/span> 0.015<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb4-4\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb4-5\" tabindex=\"-1\"><\/a>#check that power is ~80%<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb4-6\" tabindex=\"-1\"><\/a>power.prop.test<\/span>(n=<\/span>required_sample_size, p1=<\/span>baseline, p2=<\/span>baseline +<\/span> mde)<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output cell-output-stdout\">\n<pre><code>\n\n Two-sample comparison of proportions power calculation \n\n\n\n n = 10000\n\n p1 = 0.17\n\n p2 = 0.185\n\n sig.level = 0.05\n\n power = 0.7927863\n\n alternative = two.sided\n\n\n\nNOTE: n is number in *each* group<\/code><\/pre>\n<\/div>\n<\/div>\nWhat long-term detection rates should they expect to see?<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\" open=\"\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb6\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb6-1\" tabindex=\"-1\"><\/a>estimate_detection_rate =<\/span> function<\/span>(baseline, required_sample_size, density_func) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-2\" tabindex=\"-1\"><\/a> integrate<\/span>(function<\/span>(x) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-3\" tabindex=\"-1\"><\/a> p =<\/span> power.prop.test<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-4\" tabindex=\"-1\"><\/a> n=<\/span>required_sample_size, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-5\" tabindex=\"-1\"><\/a> p1=<\/span>baseline,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-6\" tabindex=\"-1\"><\/a> p2=<\/span>baseline +<\/span> x<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-7\" tabindex=\"-1\"><\/a> )$<\/span>power <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-8\" tabindex=\"-1\"><\/a> density_func<\/span>(x) *<\/span> replace_na<\/span>(p, 0<\/span>) <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-9\" tabindex=\"-1\"><\/a> }, -<\/span>Inf<\/span>, Inf<\/span>)$<\/span>value <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-10\" tabindex=\"-1\"><\/a>}<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-11\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-12\" tabindex=\"-1\"><\/a>tibble<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-13\" tabindex=\"-1\"><\/a> `<\/span>Assumed effect size distribution<\/span>`<\/span> =<\/span> sapply<\/span>(eff_size_distrs, function<\/span>(x) x$<\/span>name),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-14\" tabindex=\"-1\"><\/a> `<\/span>P(detect | effect size distribution, N, baseline)<\/span>`<\/span> =<\/span> sapply<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-15\" tabindex=\"-1\"><\/a> eff_size_distrs,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-16\" tabindex=\"-1\"><\/a> function<\/span>(x) estimate_detection_rate<\/span>(baseline, required_sample_size, x$<\/span>density)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-17\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb6-18\" tabindex=\"-1\"><\/a>) |><\/span> kbl<\/span>(caption=<\/span>'Long-run detection rates'<\/span>, digits=<\/span>2<\/span>) |><\/span> kable_styling<\/span>()<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<table class=\"table table-sm table-striped small\" data-quarto-postprocess=\"true\">\n<caption>Long-run detection rates<\/caption>\n<thead>\n<tr class=\"header\">\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: left;\">Assumed effect size distribution<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">P(detect | effect size distribution, N, baseline)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td style=\"text-align: left;\">Startup team<\/td>\n<td style=\"text-align: right;\">0.78<\/td>\n<\/tr>\n<tr class=\"even\">\n<td style=\"text-align: left;\">Mature team<\/td>\n<td style=\"text-align: right;\">0.25<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\nA massive difference! The startup team will be detecting an impact in nearly 80% of experiments, while the mature team will do so only in 25% of them!<\/p>\nWhat about win rates?<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\" open=\"\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb7\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb7-1\" tabindex=\"-1\"><\/a>estimate_win_rate =<\/span> function<\/span>(baseline, required_sample_size, density_func) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-2\" tabindex=\"-1\"><\/a> integrate<\/span>(function<\/span>(x) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-3\" tabindex=\"-1\"><\/a> p =<\/span> power.prop.test<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-4\" tabindex=\"-1\"><\/a> n=<\/span>required_sample_size, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-5\" tabindex=\"-1\"><\/a> p1=<\/span>baseline,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-6\" tabindex=\"-1\"><\/a> p2=<\/span>baseline +<\/span> x<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-7\" tabindex=\"-1\"><\/a> )$<\/span>power <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-8\" tabindex=\"-1\"><\/a> density_func<\/span>(x) *<\/span> replace_na<\/span>(p, 0<\/span>) <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-9\" tabindex=\"-1\"><\/a> }, 0<\/span>, Inf<\/span>)$<\/span>value <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-10\" tabindex=\"-1\"><\/a>}<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-11\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-12\" tabindex=\"-1\"><\/a>tibble<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-13\" tabindex=\"-1\"><\/a> `<\/span>Assumed effect size distribution<\/span>`<\/span> =<\/span> sapply<\/span>(eff_size_distrs, function<\/span>(x) x$<\/span>name),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-14\" tabindex=\"-1\"><\/a> `<\/span>P(detect a win | effect size distribution, N, baseline)<\/span>`<\/span> =<\/span> sapply<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-15\" tabindex=\"-1\"><\/a> eff_size_distrs,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-16\" tabindex=\"-1\"><\/a> function<\/span>(x) estimate_win_rate<\/span>(baseline, required_sample_size, x$<\/span>density)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-17\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb7-18\" tabindex=\"-1\"><\/a>) |><\/span> kbl<\/span>(caption=<\/span>'Long-run win rates'<\/span>, digits=<\/span>2<\/span>) |><\/span> kable_styling<\/span>()<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<table class=\"table table-sm table-striped small\" data-quarto-postprocess=\"true\">\n<caption>Long-run win rates<\/caption>\n<thead>\n<tr class=\"header\">\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: left;\">Assumed effect size distribution<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">P(detect a win | effect size distribution, N, baseline)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td style=\"text-align: left;\">Startup team<\/td>\n<td style=\"text-align: right;\">0.62<\/td>\n<\/tr>\n<tr class=\"even\">\n<td style=\"text-align: left;\">Mature team<\/td>\n<td style=\"text-align: right;\">0.23<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\nWe get 62% and 24%, respectively. The latter number isn\u2019t far off an <a href=\"https:\/\/hdsr.mitpress.mit.edu\/pub\/aj31wj81\/release\/1\">often-quoted<\/a> figure for the win rate in online experiments of <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mn>10<\/mn><mo>\u2212<\/mo><mn>20<\/mn><mi>%<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">10-20\\%<\/annotation><\/semantics><\/math>. That\u2019s worth pondering: we know that the mature team produces winning ideas 75% of the time, but because most effects are minor, the actual observable win rate is just 24%.<\/p>\nRemember, these results were obtained using a sample size that \u201cprovides 80% power under an MDE of 1.5%\u201d! But our \u201cactual power\u201d is closer to 30% (0.24\/0.75). The MDE choice in the case of the mature team is clearly questionable.<\/p>\n<\/section>\n<\/section>\n<section class=\"level2\" id=\"impact-of-sample-size\">\n<h2 data-anchor-id=\"impact-of-sample-size\">Impact of sample size<\/h2>\nHow do win and detection rates change with the sample size? We can plot win and detection rates as a function of <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>n<\/mi><annotation encoding=\"application\/x-tex\">n<\/annotation><\/semantics><\/math>.<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb8\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb8-1\" tabindex=\"-1\"><\/a>sample_sizes =<\/span> seq<\/span>(1000<\/span>, 200000<\/span>, 2000<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-2\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-3\" tabindex=\"-1\"><\/a>win_and_detection_rates =<\/span> pblapply<\/span>(sample_sizes, function<\/span>(n) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-4\" tabindex=\"-1\"><\/a> lapply<\/span>(eff_size_distrs, function<\/span>(d) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-5\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-6\" tabindex=\"-1\"><\/a> name =<\/span> d$<\/span>name, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-7\" tabindex=\"-1\"><\/a> detection_rate =<\/span> estimate_detection_rate<\/span>(baseline, n, d$<\/span>density),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-8\" tabindex=\"-1\"><\/a> win_rate =<\/span> estimate_win_rate<\/span>(baseline, n, d$<\/span>density), <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-9\" tabindex=\"-1\"><\/a> n=<\/span>n<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-10\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-11\" tabindex=\"-1\"><\/a> })<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-12\" tabindex=\"-1\"><\/a>}, cl=<\/span>4<\/span>) |><\/span> bind_rows<\/span>()<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-13\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-14\" tabindex=\"-1\"><\/a>mde_sample_sizes =<\/span> lapply<\/span>(c<\/span>(0.004<\/span>, 0.005<\/span>, 0.0075<\/span>, 0.01<\/span>, 0.015<\/span>), function<\/span>(mde) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-15\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-16\" tabindex=\"-1\"><\/a> n=<\/span>power.prop.test<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-17\" tabindex=\"-1\"><\/a> p1=<\/span>baseline, p2=<\/span>baseline +<\/span> mde, power=<\/span>0.8<\/span>, sig.level=<\/span>0.05<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-18\" tabindex=\"-1\"><\/a> )$<\/span>n, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-19\" tabindex=\"-1\"><\/a> mde=<\/span>mde<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-20\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-21\" tabindex=\"-1\"><\/a>}) |><\/span> bind_rows<\/span>()<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-22\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-23\" tabindex=\"-1\"><\/a>ggplot<\/span>(win_and_detection_rates) +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-24\" tabindex=\"-1\"><\/a> geom_line<\/span>(aes<\/span>(x=<\/span> n, y=<\/span>detection_rate, color=<\/span>name, linetype=<\/span>\"Detection rate\"<\/span>)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-25\" tabindex=\"-1\"><\/a> geom_line<\/span>(aes<\/span>(x=<\/span> n, y=<\/span>win_rate, color=<\/span>name, linetype=<\/span>'Win rate'<\/span>)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-26\" tabindex=\"-1\"><\/a> geom_hline<\/span>(aes<\/span>(yintercept=<\/span>0.75<\/span>, color=<\/span>'Startup team'<\/span>, linetype=<\/span>'Share of positive-impact experiments'<\/span>)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-27\" tabindex=\"-1\"><\/a> geom_hline<\/span>(aes<\/span>(yintercept=<\/span>0.74<\/span>, color=<\/span>'Mature team'<\/span>, linetype=<\/span>'Share of positive-impact experiments'<\/span>)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-28\" tabindex=\"-1\"><\/a> theme_light<\/span>() +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-29\" tabindex=\"-1\"><\/a> theme<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-30\" tabindex=\"-1\"><\/a> panel.grid.major =<\/span> element_blank<\/span>(),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-31\" tabindex=\"-1\"><\/a> legend.position =<\/span> \"right\"<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-32\" tabindex=\"-1\"><\/a> ) +<\/span> labs<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-33\" tabindex=\"-1\"><\/a> x =<\/span> 'Sample size (per group)'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-34\" tabindex=\"-1\"><\/a> y =<\/span> ''<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-35\" tabindex=\"-1\"><\/a> color =<\/span> 'Effect size prior assumption'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-36\" tabindex=\"-1\"><\/a> title =<\/span> 'Avg detection and win rates as function of sample size'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-37\" tabindex=\"-1\"><\/a> linetype =<\/span> 'Metric'<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-38\" tabindex=\"-1\"><\/a> ) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-39\" tabindex=\"-1\"><\/a> scale_x_continuous<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-40\" tabindex=\"-1\"><\/a> sec.axis =<\/span> dup_axis<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-41\" tabindex=\"-1\"><\/a> name =<\/span> 'MDE that results in 80% power'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-42\" tabindex=\"-1\"><\/a> breaks =<\/span> mde_sample_sizes[['n'<\/span>]],<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-43\" tabindex=\"-1\"><\/a> labels =<\/span> scales::<\/span>percent<\/span>(mde_sample_sizes[['mde'<\/span>]]),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-44\" tabindex=\"-1\"><\/a> guide =<\/span> guide_axis<\/span>(n.dodge =<\/span> 3<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-45\" tabindex=\"-1\"><\/a> ),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-46\" tabindex=\"-1\"><\/a> breaks =<\/span> c<\/span>(1000<\/span>, seq<\/span>(0<\/span>, 200000<\/span>, 5000<\/span>)),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-47\" tabindex=\"-1\"><\/a> labels =<\/span> c<\/span>(\"1,000\"<\/span>, sapply<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-48\" tabindex=\"-1\"><\/a> seq<\/span>(0<\/span>, 200000<\/span>, 5000<\/span>), <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-49\" tabindex=\"-1\"><\/a> function<\/span>(x) if_else<\/span>(x %%<\/span> 40000<\/span> ==<\/span> 0<\/span> &&<\/span> x ><\/span> 0<\/span>, format<\/span>(x, big.mark =<\/span> \",\"<\/span>), \"\"<\/span>))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-50\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-51\" tabindex=\"-1\"><\/a> ) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-52\" tabindex=\"-1\"><\/a> scale_y_continuous<\/span>(breaks =<\/span> seq<\/span>(0<\/span>, 1<\/span>, 0.1<\/span>), labels =<\/span> scales::<\/span>percent_format<\/span>()) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-53\" tabindex=\"-1\"><\/a> scale_linetype_manual<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-54\" tabindex=\"-1\"><\/a> values =<\/span> c<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-55\" tabindex=\"-1\"><\/a> \"Detection rate\"<\/span>=<\/span>\"solid\"<\/span>, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-56\" tabindex=\"-1\"><\/a> \"Win rate\"<\/span> =<\/span> 'longdash'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-57\" tabindex=\"-1\"><\/a> \"Share of positive-impact experiments\"<\/span> =<\/span> 'dashed'<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-58\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb8-59\" tabindex=\"-1\"><\/a> )<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<div>\n<figure class=\"figure\">\n<img decoding=\"async\" class=\"img-fluid figure-img\" 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width=\"768\"\/><\/p>\n<\/figure>\n<\/div>\n<\/div>\n<\/div>\nThere\u2019s a lot going on in this chart, but here\u2019s a few key takeaways:<\/p>\n<ul>\n<li>The start-up team reaches ~90% detection rates (and, by extension, approaches its true win rate) as soon as the experiment sample size exceeds 40,000 observations per group. There\u2019s little to be gained beyond that.<\/p><\/li>\n<li>At the same time, if this were an early-stage start-up with just a few thousand customers to experiment with, it\u2019s very possible it would see win\/detection rates of just 50-60%. It\u2019s a tough trade-off between the speed in decision-making that\u2019s key in a start-up context and a higher share of false positives.<\/p><\/li>\n<li>In the case of the mature team, there\u2019s most to be gained until sample sizes reach ~80,000 observations per group. That\u2019s an MDE of ~0.5%! Previously, we saw that using a 1.5% MDE (or 10,000 users per group) yields an observable win rate of 24%; increasing the sample size to 80,000 would double the observable win rate. Granted, most of these wins would be small, but as discussed above, that may still be a lot of money.<\/p><\/li>\n<li>The mature team would have difficulty seeing win rates close to its true success rate (74%). It would take 5,000,000 observations per group to reach a win rate of 70%.<\/p><\/li>\n<\/ul>\n<\/section>\n<section class=\"level2\" id=\"what-about-the-type-i-error-rate-in-this-context\">\n<h2 data-anchor-id=\"what-about-the-type-i-error-rate-in-this-context\">What about the Type I error rate in this context?<\/h2>\nWe saw how to go from statistical power \/ Type II error rate to long-term detection\/win rates. Can we do the same with the Type I error rate?<\/p>\nWell, the thing is that the Type I error rate is only relevant if some experiments have zero impact. It\u2019s not \u201cclose enough\u201d to zero, but literally zero. In reality, (hopefully!) you don\u2019t run such experiments. It works for individual experiments because the frequentist hypothesis testing framework asks \u201chow likely we would have observed data like the one at hand if the true effect was zero\u201d and not \u201chow likely the true effect is zero.\u201d But this question doesn\u2019t translate to a series of experiments.<\/p>\nAdditionally, the Type I error rate does not depend on the data you collect (sample size, variance). Even if you assume that some experiments have precisely zero impact, all you can do is choose Type I error rate with <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b1<\/mi><annotation encoding=\"application\/x-tex\">\\alpha<\/annotation><\/semantics><\/math>; you can\u2019t influence it beyond that choice.<\/p>\nAs a result, there\u2019s nothing we can optimize \u00af\\_(\u30c4)_\/\u00af. You could ask deeper questions about the usefulness of an independent-of-data error rate that doesn\u2019t apply in most situations, which would land you into the world of \u201cIs null hypothesis testing the right thing to do?\u201d but we\u2019re not going there today.<\/p>\nWhat are the alternatives?<\/p>\nFor one, we can estimate expected confidence interval widths and quantify the average uncertainty in our results. In a <a href=\"https:\/\/stats.stackexchange.com\/questions\/240452\/does-the-width-of-the-confidence-interval-necessarily-imply-power\">simple t-test setting<\/a>: <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>C<\/mi><msub><mi>I<\/mi><mtext mathvariant=\"normal\">width<\/mtext><\/msub><mo>=<\/mo><mn>2<\/mn><mi>M<\/mi><mi>D<\/mi><mi>E<\/mi><mrow><mo form=\"prefix\" stretchy=\"true\">(<\/mo><mi>\u03b2<\/mi><mo>=<\/mo><mn>0.5<\/mn><mo form=\"postfix\" stretchy=\"true\">)<\/mo><\/mrow><\/mrow><annotation encoding=\"application\/x-tex\">CI_{\\text{width}} = 2 MDE(\\beta=0.5)<\/annotation><\/semantics><\/math>, i.e., the widths are twice the MDE that yields 50% power. Or, graphically:<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb9\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb9-1\" tabindex=\"-1\"><\/a>sample_sizes =<\/span> seq<\/span>(1000<\/span>, 200000<\/span>, 1000<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-2\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-3\" tabindex=\"-1\"><\/a>ci_widths =<\/span> lapply<\/span>(sample_sizes, function<\/span>(n) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-4\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-5\" tabindex=\"-1\"><\/a> n=<\/span>n,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-6\" tabindex=\"-1\"><\/a> ci_width =<\/span> (power.prop.test<\/span>(p1=<\/span>baseline, power=<\/span>0.5<\/span>, n=<\/span>n)$<\/span>p2 -<\/span> baseline) *<\/span> 2<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-7\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-8\" tabindex=\"-1\"><\/a>}) |><\/span> bind_rows<\/span>()<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-9\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-10\" tabindex=\"-1\"><\/a>ggplot<\/span>(ci_widths) +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-11\" tabindex=\"-1\"><\/a> geom_linerange<\/span>(aes<\/span>(x=<\/span>n, ymin=<\/span>-<\/span>ci_width\/<\/span>2<\/span>, ymax=<\/span>ci_width\/<\/span>2<\/span>)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-12\" tabindex=\"-1\"><\/a> theme_light<\/span>() +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-13\" tabindex=\"-1\"><\/a> theme<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-14\" tabindex=\"-1\"><\/a> panel.grid.major =<\/span> element_blank<\/span>(),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-15\" tabindex=\"-1\"><\/a> legend.position =<\/span> \"right\"<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-16\" tabindex=\"-1\"><\/a> ) +<\/span> labs<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-17\" tabindex=\"-1\"><\/a> x =<\/span> 'Sample size (per group)'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-18\" tabindex=\"-1\"><\/a> y =<\/span> 'CI width'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-19\" tabindex=\"-1\"><\/a> title =<\/span> 'Confidence interval widths as a function of sample size'<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb9-20\" tabindex=\"-1\"><\/a> )<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<div>\n<figure class=\"figure\">\n<img decoding=\"async\" class=\"img-fluid figure-img\" 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09zrZy\/r6vgP7Xjfj51KBWBw6j1gX4z9OhC\/VbvZzXIvPD0Ao0Q+G3\/ev5v1AG6lCsCqDXNeBnrSttseHh0Vbk7Ompp6zJ64e+UNwFx7R0m97eiU2EFubNb9Qla412bv+K9kjsa5ZEpaAHJGljg8OVcxNuMFiVeKHBT7Fn9v1yO7xM+77ffyB+Bdke2jvwW\/a56J\/vq7t8hzqVE5t0SedwInAmDmnzo8dsGDRn1osjgGVfD5U7oLQPe3TW4AerzXJDmXcL4TOPeaOq5fNwHYJrW6+J8x4rdqN7tZ7oWnBWC8yHovx093tx7AqWTvAzgo\/XfWyGeiJ0cOSX+E\/ET8rtOTAKyQ9Me9Y2OHPXuQ\/OHV6Va61\/bZ9CdNIs4lU3oSAOcqxmZ8DoN96D9kiTF7icTeH3R9P3tX3\/v0G5+aY+7NHwCzQ3iM+bpsaa\/1UPm0WTZYNlmdGpVzS3QXALvY5HP2jd5wNTzzUOkcVC\/On+AMwKD8Aci9bXID0OO9Jsm5RJ6Pgsi6pq7r1\/PfAL4Suznjt2o3u1nuhacC8PgAkdsS5+p+PYBDyQJwtb0TZXz6Z7tEvxMcH3+NY+T6+MsnehIAU5N+oIi\/rPLi5Pvk0zjW9tWc30ecS6b0JADOVWQGwNSHrx6c2T\/+3t559p5+ePzY3VUATrYPX5cOlMNiV3T6E4l3VcdH5doS3QcgtPKZ\/xuc\/ZlMzkH18vwhVwA+lPwByL1tcgPQ470myblEF58FlLqmzuvXTQDSglwTG3H8Vu1mN8u58FQAwj8A\/zJ5jp6tB8hSsgAs3likMX3G6SIDO435i8hXk\/NOjJ+nRwH4jsi4xJlWbhY77D2a\/nqIRdOmdeRZ28\/S\/i769y23bMyzZEpPAuBcRVYArgmP3fZ3+T9GUzfYI0biAHRHFwF4WGTM47G3AFxoD4\/nidySPirXlug6AHNnJz\/GbLLIgIyPEnANqtDzp0sPQOIjep7sIgC5t01uAHq81yQ5l3AEIPeaOq9fdwFIvgrodZF+4R9147dq17uZYzMndrzwD8D7pMbaze4KuJXu46B\/Z+8Wf0tNvr6eyCEmeumcPB6f17Fe\/JfcHgWg2f6WnHj1u717R4e9xUNEkv8\/40iRK\/Oszd6RN0h8NN2xIpfmWTKlJwFwriIrAPPWlU+s+IYMiD0MPy\/tpSm\/7iIAHw+Wz4wLP+os+gy9o\/eV\/nPSR+XaEl0HIEi7gnbU\/0u\/MNegCj1\/uvQA3Buf97MuApB72+QGoMd7TZJzCUcAcq+p8\/p1F4AvJ5b4cfyXjfit2vVu5tjMiR1vlMhWaX986WZ3BdxKF4ClX7KPaa5KPDf6+vZ2KnpZ0LftQ7PYSx5W2x35k9H733sUgNnr2sfFsbvv9C0Sh71Ge9+LvwV08joyaF6eta3YPPmMwKv2l+0X8iyZ0k0ANjX5LjwrAOYHItf2S7wu\/3qR78fntw3tIgDmeyI7yfBw460cKlsPSTyYjY\/KuSWSo3IF4NzU8zj\/ERmU8YjeNahCz58ueZAdnfwdcMo6XQTAcdukrkvidujpXpPiWsIRgNxr6rx+3QUg8VkT9\/UTeTA8kbhVu9zNHJs5foUvs5MZn3Da9e4KuJXwX0L+b2N7v\/jaX+2uuvK\/vww\/s+bCaPaz9mCw04vhz78raZ\/q0n0AwieRpP5Ne8SYtLEMiB\/2\/vcJkd3C112vuGZo4jjiWpv9vV4OCX9v\/metyH75lkzpJgCDluZbRXYAbhSx57ojNjFFpN\/14YnFvwo\/\/iv2S70rAJeGx5QR0cnvhSfPzRyVa0skR+UKwGS7QMO79sTqBzfP\/phW16AKPX+65EH2cnvGa8IT99VIFwFw3Dap65K4xj3da1JcSzgCkHtNndev2wDIWXZHnzeuf+Idw4lbtcvdzLGZY1d4sr1Vr8s4a9e7K+BWyn8K\/8pnonvG+pv1j77HXwhpLgknavaMPq84fmjpWQCWRh\/Cu\/Hu9mHiwPGJ17rfG34O71b77BEeY76zIu\/azNHhstvs\/Wn79ZOz8y2Z0lUAjL0v7nHC0e5VZAdgQXgQqUl8nNqP7MTOR4z+6jCRw+xxpiFctysAU8PRxl7HfmF48j+Zo3JuieSoXH8Ethci\/bbaa4\/wdec7z8+8sDyDKuj8aZIH2Y5P2TPudMSP7GF9wKe7CIDjtklel+TfRHu416RxLOH6I3DuNXVdv24CsPlO9ow77hB+ivQG\/02sNnardrmb5V547Arbe84mp6Qs7m49gFspA2DmjBkgCZ9O\/T3g2g3i89Y9I\/XJ7j0IgFk0Mr5gzaSXkm92+kfik+\/XOXpB\/rWZNb9KfGL7NxKvrc5dMqXLAPw0XGhD9yqyA2AOtD\/9WWJi\/jfi59\/sb+GdPPoIS+f\/jtra\/jD2z1yeDQ\/1qzNH5dwSyVG5ArDkkORH3H836yP8nYMq9PxpUgfZJzZKrOOqA7sKQO5tk7wuqVdF9myvSZe7hCsAudfUdf26CcCOs+L\/IUA+G3\/iJnWrdrWb5V547ApvLBnmdrcewK2kATDmnYu\/95kN1t1kl2Na0l+3PKd5z83W3ehLZ76dmNHDABjzz6O2XW+Dnc+aYVIBMB9ftd+Wg7bY+8Q3ulqb9VbTbsOHfenwW1Iv2c5ZMqXLACz8v23WHfZV9ypyAnB78lAeaQl2GPTJvS5dZMwLX19vg\/CtEs4AHCeyXuxh3sphqY8XTXuXUO6WSI7K\/TLQl8bsufWgrfc+1vW\/s3IHVfj5k9IOsvPP\/mb4cfyfvNN0GYDc2yZ5XdKucY\/2mgw5S7hfBpp7TXOvX3cBMKtv\/O5mAz+171WJ3\/XSbtUudrPcC88fgK7XA7iUOACoegufn5L9fyPXMlEAgHJEAIC+RQBQtggA0LcIAMoWAQD6FgFA2SIAQN8iAChbBADoWwQAZYsAAH1r1jbb7FvqMQBOBAAAqhQBAIAqRQAAoEoRAACoUgQAAKoUAQCAKkUAAKBKEQAAqFIEAACqFAEAgCpFAACgShEAAKhSBAAAqhQBAIAqRQAAoEqVKADPPLmWmlzqAVS4J9iAOpOfKPUIKtw\/Sz2APjarLALwyONta6d\/PV\/qEVS25\/9V6hFUuGeeK\/UIKtzTpR5An3p+0v\/KIwBvluZy+9yHq0s9gsq2ak6pR1Dh5q4o9Qgq25rZpR5Bn1pNAPoWAdAhAEoEQIcAeEEA4EQAlAiADgHwggDAiQAoEQAdAuAFAYATAVAiADoEwAsCACcCoEQAdAiAFwQATgRAiQDoEAAvCACcCIASAdAhAF4QADgRACUCoEMAvCAAcCIASgRAhwB4QQDgRACUCIAOAfCCAMCJACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8IIAwIkAKBEAHQLgBQGAEwFQIgA6BMALAgAnAqBEAHQIgBcEAE4EQIkA6BAALwgAnAiAEgHQIQBeEAA4EQAlAqBDALwgAHAiAEoEQIcAeKELwPUNDS8VayRFRgB0CIASAdAhAF7oAnCyyCPFGkmREQAdAqBEAHQIgBcEAE4EQIkA6BAALwgAnAiAEgHQIQBeEAA4EQAlAqBDALwgAHAiAEoEQIcAeEEA4EQAlAiADgHwggDAiQAoEQAdAuAFAYATAVAiADoEwAsCACcCoEQAdAiAFwQATgRAiQDoEAAvCACcCIASAdAhAF4QADgRACUCoEMAvCAAcCIASgRAhwB4QQDgRACUCIAOAfCCAMCJACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8IIAwIkAKBEAHQLgBQGAEwFQIgA6BMALAgAnAqBEAHQIgBcEAE4EQIkA6BAALwgAnAiAEgHQIQBeEAA4EQAlAqBDALwgAHAiAEoEQIcAeEEA4EQAlAiADgHwggDAiQAoEQAdAuAFAYATAVAiADoEwAsCACcCoEQAdAiAFw\/\/d45Co0iLZvk+NOPDUo+gsn04o9QjqHAzOko9gsr24XulHkGf+nDStLIIwCNTVyr8XORBzfJ9qGN5qUdQ2ZZ1lHoEFe7DpaUeQWVbMavUI+hTyydNL48A8BQQXHgKSImngHR4CsgLAgAnAqBEAHQIgBcEAE4EQIkA6BAALwgAnAiAEgHQIQBeEAA4EQAlAqBDALwgAHAiAEoEQIcAeEEA4EQAlAiADgHwggDAiQAoEQAdAuAFAYATAVAiADoEwAsCACcCoEQAdAiAFwQATgRAiQDoEAAvCACcCIASAdAhAF4QADgRACUCoEMAvCAAcCIASgRAhwB4QQDgRACUCIAOAfCCAMCJACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8IIAwIkAKBEAHQLgBQGAEwFQIgA6BMALAgAnAqBEAHQIgBcEAE4EQIkA6BAALwgAnAiAEgHQIQBeEAA4EQAlAqBDALwgAHAiAEoEQIcAeEEA4EQAlAiADgHwggDAiQAoEQAdAuAFAYATAVAiADoEwAsCACcCoEQAdAiAFwQATgRAiQDoEAAvCACcCIASAdAhAF4QADgRACUCoEMAvCAAcCIASgRAhwB4QQDgRACUCIAOAfCCAMCJACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8IIAwIkAKBEAHQLgRTECsKK9vfxuLAKgQwCUCIAOAfCiGAGYKnJgscZTNARAhwAoEQAdAuAFAYATAVAiADoEwAsCACcCoEQAdAiAFwQATgRAiQDoEAAvCACcCIASAdAhAF4QADgRACUCoEMAvCAAcCIASgRAhwB4QQDgRACUCIAOAfCCAMCJACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8IIAwIkAKBEAHQLgBQGAEwFQIgA6BMALAgAnAqBEAHQIgBcEAE4EQIkA6BAALwgAnAiAEgHQIQBeEAA4EQAlAqBDALwgAHAiAEoEQIcAeEEA4EQAlAiADgEoyNJrxjSccfuq\/PPevfTog48d93r2cgQATgRAiQDoEIBCzDkpCI4IgjMX5pv31IFBMGZkMOKWrAUJAJwIgBIB0CEAhTgrOGOO+eCE4NI88+aPDC6eZ1bdM2LElMwFCQCcCIASAdAhAAWYGhwWPs6ffeCID93z7ghOj54KmhhckLkkAYATAVAiADoEoAATgz9E388M7nfPuzB4KJqYGhyVuSQBgBMBUCIAOgSgAGcFk6Pvd6Q9B5Qxb9xRL0cT04JRmUsSADgRACUCoEMACjA6eCX6Pjk4tct55pbgt5lLEgA4EQAlAqBDAArQEEyPvj8fnNDlvJcODmK\/CZhFbTEPEwC4EAAlAqBDAApwYNARfX8tGNPFvGU3HhDcHj+98NkYfgOAEwFQIgA6BKAAP00+2j8l\/7ynjwoaHslekgDAiQAoEQAdAlCAk4NXo++T057iz5q3oDkILurIWZIAwIkAKBEAHQJQgHOCJ6PvdwdX5JnXMTo4capjSQIAJwKgRAB0CEABJgZ\/ir6fEzzonrfk+GC8c4csYgBubW5epFlZcREAHQKgRAB0CEABXglGLbPfFhw8crF73n3ZL\/9MKGIA9hGZqVlZcREAHQKgRAB0CEAhTg0uXmWWnRmMDyceuP\/t7HknBlNWx2UuSADgRACUCIAOASjEzCOCH582Mjgu+uTPkcGdWfNWHRAkNGYuSADgRACUCIAOASjIvMtHHzRm4pLodDwAafNmBQQABSEASgRAhwB4QQDgRACUCIAOAfCCAMCJACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8IIAwIkAKBEAHQLgBQGAEwFQIgA6BMALAgAnAqBEAHQIgBcEAE4EQIkA6BAALwgAnAiAEgHQIQBeEAA4EQAlAqBDALwgAHAiAEoEQIcAeEEA4EQAlAiADgHwougBmNrc\/GSRxqZCAHQIgBIB0CEAXhQ9AC0ivy7S2FQIgA4BUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8IIAwIkAKBEAHQLgBQGAEwFQIgA6BMALAgAnAqBEAHQIgBcEAE4EQIkA6BAALwgAnAiAEgHQIQBeEAA4EQAlAqBDALwgAHAiAEoEQIcAeNFXATi64ejiDLC3CIAOAVAiADoEwIu+CsDGsnFxBthbBECHACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8IIAwIkAKBEAHQLgBQGAEwFQIgA6BMCLvg3A9fX1TxRlmIUjADoEQIkA6BAAL\/o2AL8WaSnKMAtHAHQIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8IIAwIkAKBEAHQLgBQGAEwFQIgA6BMALAgAnAqBEAHQIgBc+ArCstfWFogy2EARAhwAoEQAdAuCFjwB8IPLtogy2EARAhwAoEQAdAuAFAYATAVAiADoEwAsCACcCoEQAdAiAFwQATgRAiQDoEAAvCACcCIASAdAhAF4QADgRACUCoEMAvPAYgLuam+cVY8g9QwB0CIASAdAhAF54DMABIm8UY8g9QwB0CIASAdAhAF4QADgRACUCoEMAvCAAcCIASgRAhwB4QQDgRACUCIAOAfCCAMCJACgRAB0C4IX3ADxRWzuuGAPvDgHQIQBKBECHAHjhPQAPi5xSjIF3hwDoEDuRaMsAACAASURBVAAlAqBDALwgAHAiAEoEQIcAeEEA4EQAlAiADgHwolQBeK\/9nSKMvgsEQIcAKBEAHQLgRakCUCsDizD6LhAAHQKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXjzyxmqFk0QeXr36NZED7MTeIu+vXv03kbPthA2A\/Xq2yN9Wr54hso+dGCHy+urVD4mcbCdsAOzXJXPnfqy5\/C50rOyjFVeJFR+WegQVbs6yUo+gsq2aVeoR9KmVk6aXRQAefrFD4ViR2zs6nhbZz058Q+Tljo5rRH5hJ4bLcPv1FyLXdHRMEfmmnfi+yL86Om4TabQTW8u69utvRC7VXH4X3uuj9VaL2WxAnfdmlXoEFe5\/pR5An5o9aVpZBKC0TwFdLHKt\/jo48RSQDk8BKfEUkA5PAXlBAOBEAJQIgA4B8KIcArC8qely\/TXJQgB0CIASAdAhAF6UQwAWieyuvyZZCIAOAVAiADoEwAsCACcCoEQAdAiAF2UUgEnNzTPV1yeJAOgQACUCoEMAvCijAPxU5Dn19UkiADoEQIkA6BAALwgAnAiAEgHQIQBelF0AXquv\/736WhkCoEUAlAiADgHwouwC8C+Ro9XXyhAALQKgRAB0CIAXBABOBECJAOgQAC\/KNQA\/rK1drrpiBECHACgRAB0C4EW5BmA3kWWqK0YAdAiAEgHQIQBelHcA\/tHQ8GAvh0YAdAiAEgHQIQBelHcArhK5tJdDIwA6BECJAOgQAC8qIgDfqR9d8NAIgA4BUCIAOgTAi4oIQH\/ZvuChEQAdAqBEAHQIgBcVFIDf1Nb+q+dDIwA6BECJAOgQAC8qKAAnijxqzPL29h7tGQRAhwAoEQAdAuBFxQXgFZFDejI0AqBDAJQIgA4B8KJSA3BVU9PiLodGAHQIgBIB0CEAXlRqAL4l8mGXQyMAOgRAiQDoEAAvKjsAt4j8Ns\/QCIAOAVAiADoEwIu1IgBHNjTmDI0A6BAAJQKgQwC8WCsC8An5lP36ckvL+6mhEQAdAqBEAHQIgBdrUQDOFLnbmJXt7dF\/FiYAOgRAiQDoEAAv1roA\/E\/ku+HQCIAOAVAiADoEwIu1NQA\/FJmmuWZVjwAoEQAdAuDF2h2AR2pqfq25flWMACgRAB0C4MXaHYBJIqfaiV1qttBcy6pEAJQIgA4BiHv\/icdTij6OqgjAVjLUfr1A5Ea7X3V2LtRc5WpBAJQIgA4BiDyzraQr+jiqLgAfiexhJ95pa1uiueZrPQKgRAB0CECofaAQgL4IwGEiLxozc8KEJzXXfy1GAJQIgA4BCB0pcvC9L76UVPRxVHcAnhAJ30N8bmMjLxrNQgCUCIAOAQjtKAf17TgIgJ3YWWSVMTfV1t5kJ9rbZ2g2ydqCACgRAB0CEBosD\/XtOAiASQTgcpHL7YFPZGc769L6+pftRGdn1x86vfYiAEoEQIcAhDYJj4l9iQAYZwAaRZ4w5r8ih9qJx1palms2UyUiAEoEQIcAhPaTCX07DgJgug\/A10Q67ZkbGuz6zUed8zWbrFIQACUCoEMAQg\/Jzov6dBwEwPQ0ADeKXGAnhspW9uuDTU1TNVuu7BEAJQKgQwAiTbLPa305DgJgehWAU0UmGTNN5Id24m\/NzQs0m7EcEQAlAqBT7QE4P2bcttJ\/z2N+G586v+jjIABGH4DviUw35h6RM+zE+U3n2K9LOzsr+88GBECJAOhUewDEqejjIACmyAH4lHzCfv2tyC3GLGxsvNhOtLe1fazZzKVAAJQIgE61B2CYU9HHQQBMHwbgQ5Fv2YlDRF6xK6uvH28nHm5p0WxyXwiAEgHQqfYAeEIAjK8APCZygp3YQfrZr5eKXGXMysbG8+zE9La2vv1Tfy8QACUCoEMAvCAApoQBWCaym504WuRfxkyprf2Fnbi8qcle8zltbbM0t4waAVAiADoEIHTZZWkTMy+7uejjIACmXALwnMhP7cSXRRYaM1HkIjuxY+2e9uv9TU32ZlrS2lr8z4LKiwAoEQAdAhDNTZ\/9qmxZ9HEQAFPOARgkn7FfTxF52Jg3RA6wEyNqauzvBs82NNxuJ26cYK+S6WxvL\/onVhAAJQKgQwCiuemz75JBRR8HATAVFoBvi3yQ3MybyEb2629E\/mZMR23tT+zEnxsbbSBmtbT8107M7+zs5U1LAJQIgE7VB6BtY0tk46SafrJN0cdBAMzaEoCZIvvYiQNFptrbVeTk5Ga+prbW\/nxZY2P4PpJ\/TpjQYczitrZ3u7xpCYASAdCp+gD82\/E2gN8VfRwEwKz9AbhE5Bp7zBepsxOjRP5jzDMiR9mJPWtq7GZ+v7U13Bs70z\/liAAoEQCdqg\/AjLMtkbNTzr2\/+OMgAKaaA\/AFkaXGXC3yRzuxjmxnv15UV2d\/\/t4hI35vJ25pbl5izKzW1nZTNR+DVyQEQKfqAxCb29evDiUAhgBkBGCsSGv4ggMZaSf2FOkw5laRc+3EhrJZuLLaWnubf1hff7qduL6paa695JaW8PVJL7e9qNmZ1jIEQIcAhI4\/vo\/HQQAMASgoAGeJ3GV\/QxD5jp3YX+RtYx4Q+aWd+LSsZ7\/+TuQGYxbU1h4cTtTXv2XMu01NdhFzx4QJq+1vtq2t75nw+aa17vPzMhEAHQLgBQEwBKAPAhDfzD8RecGYJ0WOtRO7iKw05gqRP9n9X+Rzdtb\/1dQ8bcxbdXXhlruosXGxveTm5mfsxN0tdvuaGW1tlfq0EwHQqfYAnO1U9HEQAEMASheA40QeN+YlkR\/bia+LzDPmJpHwxUrDoje9nCZyvzHtIj+wE6Nra2fYHaS+3l4Zc3rjSfbrv5qbX7ObsaXlnybxmXsrOzuXaPbpYiEAOtUeAD4NlAAQgIwAfF\/kXWPuFWmyE5vLBvbreSI3GzNH5Jt2okHkZWP+KfIzO\/GVmhr79ba6ujtsE+rrw5v5xsZGu0\/Mam4Of7d4tKXFDmZu7BM33mnPujcWAQHQqfYAJF79PzA68K8Xftnp0EOLPg4CYAjA2hiAHaNHS5eJ\/NmY5SK72okxIk8b87zIEcnNfJ1Is50YHL3D5v9EHrIxqK0NB3t8XV1H9Ibr2+zEuKZwN3uqudlu2aUtLZPtxJTW1sXGfNzePjfPfYMA6FR7AOLuGiyfu+bNpebDf+wrg+4o\/jgIgCEABCAtAG+KjLAT+4q8b8wdIuHTrp+U4fbrOSK3298gRPa2EweJvG7M30XCZ6I+K+var1fU1NgdbGld3eF24qK99rU3w1sNDVfaifFNTTYHLzU32x3c3NFiL8W80dpqr+zy9vbwOLews3N17+6Bay8CEHpjsPx8Tfz0hTKk+P8dkgAYAkAAihKA34v8xZglIl+yE0eK\/Nv+BiEy2k58UeRjYyaI\/CG5mX8u8g9jXhMJXyy1l4g93N1VUxMOZp+6cMtOrK9\/ypjOxsbwDnBnc7PdASfX1oZbZv+6r9uv9zQ0TLa1aWq6wk7c39w8y26Glpbn7cSzra2JQ0YlIwChY+SbqYcG9dHOVFwEwBAAAlAWAbhN5Bw7USObJjfzDJF6OxGIvGXMgyLh54VvLUNM+HBQrjdmvshX7cThIm3GPCVyjJ34vIj9deP++vp77FG0vj68N11WX\/+S3Yz19ePsxNkNDfaaP9nQEL40d1TDcfbrXY2N9udzGhvtLWMWRb+OrOjsLO1\/sSMAoa2j\/TPuj3waKAEgAASgJwG4UmS8PYqK7GRnHS8y2ZgpIj+yE98QmWvMX0XCGqwvW9ivp4vcZ8w7IvslN\/N9IuE7\/T5XE+7t19bV2S2zoL4+3KcmNDbaLTOtqckGxny1psb+unFDbe1fo7+1hzfzVQ0Ndgf8X2OjvWubK6L\/bvFyc\/OzdmLiBHufMC+1tMyMPtw8\/LTCt9ralttds73d3vJmfudHqWMLAQgNCo9ZCTdH9+biIgCGABAAApAnAFvI+vbrOBF7gJ8r8g078SORKcnNvJOIDcB4kSvtLw0inzfhsxbylDFtIuGfQr4issCY60UutBNDZGv79RciD9ojv0hgwmc1ZIYxd4r8yk5sKjWxzXybvevUfGL\/8KBQU2MPGk\/V1oY74IF1X7Ff76ivt8svb2iI3ore2PiO3RubmuwdwPwp\/U8t10wI\/3z\/35YWu2UXt7baIZu21lZbm862NnsHMu+22\/us+bizc3n2Uc2LHgZgEzkxNfHz6A5YXATAEAACQADKLQCzRfayEweLvGbMP0R+bie2lQHJzfyxyBftxGiRZ6MPzjzSTnxJZIkxfxEJP8ZqgGxrv54k8ndjXhc5yE7sLTLLmNtjm3m4fNJ+\/ZXInca8L7KvnRghYg+HrTU1Z4W7fm245W6oq7M74OK6ulHhNq+vtz9\/o6HhqvAI0NCw0vReDwPwfal5O3F62vDoaFNcBMAQAAJAAAhAIgAPifyfndhGBtuvzSLXGbNAJPzd4wiR5415WmSMndhVRPO7Qw8DcL\/Ijv+OnfzPTtEuUWQEwBAAAkAACEBZBiDcKaX+7OuuP9tuqWjHLDICYAgAASAABKA8A7ByTP\/Ex0CsM2ap4gLzIACGABAAAkAAyjMA9q44cjN79O+3+U\/eUFxcXgTAEAACQAAIQLkGwFr44mt98OA\/QgAMASAABIAAlHEA+hABMASAABAAAkAACkcACAABIAAEoGDdBuDaa6\/9OPqaQXGJbgTAEAACQAAIQJkFQKJ9gn8IQwAIAAEgAASg6wAsvWZMwxm3r8o\/z3UGQwAIAAEgAASg\/AIwd+5cu2UXZ8m3ujknBcERQXDmwnzzXGcIEQBDAAgAASAAZRaAwpwVnDHHfHBCcGm+ea4zhAiAIQAEgAAQgLIMwDF3ze\/J2qYGh4UP7WcfOOJD9zzXGSIEwBAAAkAACEBZBkBkwLd+92K3\/+FtYvCH6PuZwf3uea4zRAiAIQAEgAAQgDINQGjTUbfN7XJtZwWTo+93pD3FkzHPdYYIATAEgAAQAAJQlgGYftOY7aIG9P\/qOf9e7TxLaHTwSvR9cnCqe57rDBECYAgAASAABKAsAxCaefvPdu4XRmD4oTfkOUtDMD36\/nxwgntezhlWL4khAIYAEAACQADKNgChOXef\/MX++d8HcGDQEX1\/LRjjnpdzhnmTYgiAIQAEgAAQgLIOwIpnmn8wLH8Afpp8gH+Ke17OGVbMiSEAhgAQAAJAAMo2AB9PPvfbQ6I\/BGwyMs9ZTg5ejb5PDn7rnuc6Q4QAGAJAAAgAASjLACx4+MxvDIy9EuhHV76Wd23nBE9G3+8OrnDPc50hQgAMASAABIAAlGUA1okO\/psfdvXULtc2MfhT9P2c4EH3PNcZIgTAEAACQAAIQFkGwB79Nxr3VrdreyUYZQ+XZsHBIxe757nOECEAhgAQAAJAAMo1ACLbHX3d292s7tTg4lVm2ZnB+HDigfvfzpmXMZGGABgCQAAIAAEoywBMufKwLWN\/AjjkshdyPss5ZeYRwY9PGxkcF37gjxkZ3JkzL2MiDQEwBIAAEAACUJYBCE2\/+fhdojeCDfvueXnPNO\/y0QeNmbgkOh0PQMa8zIkUAmAIAAEgAASgbAMQmjep6RuD+I9gBIAAEAACUH0BWPb4uXsTAAJgCAABIADVFYBlT563z+DoA+F2\/5XiEt0IgCEABIAAEICyDMDyp8ftG3sX8Gajbp2juLx8CIAhAASAABCAsgzAetHBf+A+F72kuKyuEABDAAgAASAAZRkAe\/TfduykvP8LXo8AGAJAAAgAASjLABzw53bFhfQAATAEgAAQAAJQlgHocwTAEAACQAAIAAEoHAEgAASAABCAghEAAkAACAABIAAxBIAAEAACQADKMAALa2rCn\/9fba3dwdpqa8Md8NC6uhWm9wgAASAABIAAFBCAL9VsYr9eW1trb4ZldXU\/Cc9cV2dv86n19eG95fSGBnvQeKqx8R\/h4aDpYvv1vqYmu+XmNjfbQ4O5f8IEG4j3WlrCf4\/4eKu9g5tpra12B1\/R1hYeB2e1t9stt6Kz095YZn7nR6YPEQACQAAIQCUF4KPa2gY7cUZdnR3Mqw0Ndp\/KCMDK2tpw+XF1df+1B\/j6+t+Fd8qGBnvNn2lstDuwObspzMBjzc3tduQTJthDh3mipcX+vKO1dVo40Wp3BvNBW5vdDVZN+4\/dzc2izs4uPgS5ghEAAkAACECfBeDampoJNgC1teHNdHZtrR3M1NraseFmqq21j20fqqu7Nrwf1YXH7PF1dXZv\/6CuLnyc\/WJrqx3sR21t9vp3+zh4QWdnlz\/vvTWz+2jF5YEAEAACQADyBuCS2lp715pTXx8+Dm9uaJiZDMCuNeHefn1dnd0yi+vrw2P6tY2N9nDyblNT+DnwE5ubVxd2Ny5LBMALAmAIAAEofgBO3fozLxnzcl3dOSZ6cnq53QyNja3hftp0if369IQJdv0LWlrsLW\/ebGuzD9qXx558zmtFNz9fuxAALwiAIQAEIDcAB9TU2L350bq6P4c3YH34IpL7Gxuft5uxuflWO\/H3CRM+MmZua6vdp80rbfZamqWdnctS9425mteIoOoDcKBT0cdBAAwBWNsDsKK2Nlz+9\/X1rxozvbFxop24vKnJ7mBvTJgQfsziY61P2a9z29uX2B2gs3OR5j6RQAB0qj0A4lT0cRAAQwAqKgAP1dSEg\/l+3Z726wONjfbKLG5qshvTtE6YYJfvbG2119+8197Hn6LVHQKgQwAIAAGojgC8UlMT3uZH1dUtMObZxsYH7cQfm6+0X6e0tNgru6ytzV5K+JLACvrrJgHQqfYAzHUq+jgIgCEAfReAU2prXzXm9YaGq+3EH5ua7AH81QkT7Kw1ra3P2FlLOjtXanbAMkYAdKo9AJ4QAEMAehGAmbW1o+zEhQ0Ns+wO0NT0gJ14oMX+wMxqayv+45QKRAB0CIAXBMAQAEcA2r+022nhko2NdjDTJkxosxP\/bZtiv37c2cmhrQcIgA4B8IIAGALw+oQJL9uJF9peSd20q\/riP1BXEwKgQwCsFf+KfW894JwX+2YcBMBUSwA+rqsLXwD\/t6am9+zhqaXFbmwzo719jfumJQBKBECHAJhXjqzZJnbqThH5wj\/6YhwEwKxlAfhNfb09wL\/Z1HS\/nXi69bFe3rQEQIkA6BCA29YTSQtA7INCio0AmIoMQGtdXfjS9\/Oawus\/rbW16PcWAqBEAHSqPgDX20P+br+PnV7zxl\/sQUiuLv44CIAp\/wD8ub7ebpmPYh+gO7O93ccH5BIAJQKgU+0BWPIpGXpr+owbB8vQeUUfBwEwZRaAM6JPUH+npeUNzS2jRgCUCIBOtQfgkuhwlO5qkYuLPg4CYEoYgBX19eHlPz5hQtnt7QRAiQDoVHsADpRtsvagNZ+R7xV9HATA+ArAkzU14WB+3xSea357+wLNlu9zBECJAOhUewA+H31obYaTo3\/fVlwEwPRhAObX14f\/X\/qZlpb5ms1cCgRAiQDoVHsAhkbvvs9wafR\/6ouLAJgiB6Cx4Uj7dWb0j00rGAFQIgA61R6A9aN352f4fXQcKy4CYPQBuKyxca4xi9vbK\/uYn4EAKBEAnWoPwGej\/\/+Z4Wfy6aKPgwCYXgXgpoaGF4xZ0d4+S7P9yhgBUCIAOtUegB\/LjtmLbBn9r7riIgCmpwF4oK7uOjvxYtvLmk1WKQiAEgHQqfYA3BgdO9NdIXJF0cdBAEz3AbipuXmpZjNVIgKgRAB0qj0Ay7eW4RkfAPfYENl4cdHHQQCMMwCn1NT8y5iFra1V8XDfgQAoEQCdag+AmdRfhp7ekZiacdy60QsLi40AmMwAxP9VVdUjAEoEQKfqA2CuXkdkvTF\/uOvFl+66eNQgkX4X9ME4CICdaKirW23Mss7OZZqNsXYhAEoEQIcAmMe3SP9\/8Bve3RfjqO4AvNPUdJfm+q\/FCIASAdAhAMZ8fMUOicP\/luf3zXtJqzUAj06YwBGuKwRAiQDoEIDIezdf+MtTzp\/YZ58MWXUBWNna+pzmKlcLAqBEAHQIgBdVFICP2tsXaa5sdSEASgRAhwB4URUBmN\/5keZaViUCoEQAdAiAF2t3AJa0t8\/VXL8qRgCUCIAOAfBibQ3A263P8ZpODQKgRAB0CIAXa10AlrS0PBEO7cPVmisGAqBEAHQIgBdrUQAebmp6NTU0AqBDAJQIgA4B8GKtCMBedbn\/K5MA6BAAJQKgQwC8WCsC4EIAdAiAEgHQIQBeVHYA5rW1zcwzNAKgQwCUCIAOAfCiUgPwm4aGBV0OjQDoEAAlAqBDALyo1AB0iwDoEAAlAqBDALyouAAsam2d0pOhEQAdAqBEAHQIgBcVF4CeIgA6BECJAOgQAC8qKACFIQA6BECJAOgQAC8qIgDb1u5b8NAIgA4BUCIAOgTAi4oIQG8QAB0CoEQAdAiAF+UdgDvr6m7t5dAIgA4BUCIAOgTAi\/IOgAIB0CEASgRAhwB4QQDgRACUCIAOAfCiXAOgRgB0CIASAdAhAF4QADgRACUCoEMAvCi7APynpmas+loZAqBFAJQIgA4B8KLsAlAsBECHACgRAB0C4AUBgBMBUCIAOgTAizIKwHWNje9qr04KAdAhAEoEQIcAeFFGASguAqBDAJQIgA4B8IIAwIkAKBEAHQLgRTkE4OP6+hP01yQLAdAhAEoEQIcAeFEOAegTBECHACgRAB0C4AUBgBMBUCIAOgTAi4dfmqdwnMgd8+b9W+QHduKbIq\/Pm3edyKl2YiPZyH49VeS6efNeE\/mWndhP5D\/z5tlAHG8ntpGB9uu5IuM1l9+F9+f20YqrxNz3Sz2CCvf+nFKPoLLNfa\/UI+hTcye1l0UAHnltmcJYkUnLlk0RGWEn9hKZvmzZLSJn2QkbAPv1LJFbli17V2RvOxGIvLxs2f0iP7cTn5GB9ut7zzzzgebyuzDr4z5acZX4eHapR1DhOhaXegSVbenMUo+gT3086d3MI3FVPgXUh3gKSIengJR4CkiHp4C8IABwIgBKBECHAHhBAOBEAJQIgA4B8KJUAXi69bEijL4LBECHACgRAB0C4EWpAtDnCIAOAVAiADoEwAsCACcCoEQAdAiAFwQATgRAiQDoEAAvCACcCIASAdAhAF54D4AvBECHACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXngMwEUNDR8UY8g9QwB0CIASAdAhAF54DIBfBECHACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC88BGARc3NtxVlsIUgADoEQIkA6BAAL3wEoCQIgA4BUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8KJvA\/DH2tqHijLMwhEAHQKgRAB0CIAXfRuAEiIAOgRAiQDoEAAvCACcCIASAdAhAF4QADgRACUCoEMAvCAAcCIASgRAhwB4QQDgRACUCIAOAfCCAMCJACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXvRVAPat27c4A+wtAqBDAJQIgA4B8KKvAlByBECHACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8IIAwIkAKBEAHQLgBQGAEwFQIgA6BMALAgAnAqBEAHQIgBcEAE4EQIkA6BAALwgAnAiAEgHQIQBeEAA4EQAlAqBDALwoegDaGhvvK9LYVAiADgFQIgA6BMCLogegXBAAHQKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8IIAwIkAKBEAHQLgBQGAEwFQIgA6BMALAgAnAqBEAHQIgBcEAE4EQIkA6BAALwgAnAiAEgHQIQBeEAA4EQAlAqBDALwgAHAiAEoEQIcAeEEA4EQAlAiADgHwggDAiQAoEQAdAuAFAYATAVAiADoEwAsCACcCoEQAdAiAFwQATgRAiQDoEICCLL1mTMMZt6\/KP+\/dS48++Nhxr2cvRwDgRACUCIAOASjEnJOC4IggOHNhvnlPHRgEY0YGI27JWpAAwIkAKBEAHQJQiLOCM+aYD04ILs0zb\/7I4OJ5ZtU9I0ZMyVyQAMCJACgRAB0CUICpwWHh4\/zZB4740D3vjuD06KmgicEFmUsWMQB\/aGycr1lZcREAHQKgRAB0CEABJgZ\/iL6fGdzvnndh8FA0MTU4KnPJIgagvBAAHQKgRAB0CEABzgomR9\/vSHsOKGPeuKNejiamBaMylyQAcCIASgRAhwAUYHTwSvR9cnBql\/PMLcFvM5ckAHAiAEoEQIcAFKAhmB59fz44oct5Lx0cxH4TMIunxBAAOBEAJQKgQwAKcGDQEX1\/LRjTxbxlNx4Q3B4\/\/dFjMQ8TALgQACUCoEMACvDT5KP9U\/LPe\/qooOGR7CX5DQBOBECJAOgQgB44PLAutgfi4NVoenLaU\/xZ8xY0B8FFHTlrIABwIgBKBECHAPTA78+z7jLmnODJaPru4IrkzzLndYwOTpzqWAMBgBMBUCIAOgSgABODP0XfzwkedM9bcnww3rlDEgA4EQAlAqBDAArwSjBqmf224OCRi93z7st++WcCAYATAVAiADoEoBCnBhevMsvODMaHEw\/c\/3b2vBODKavjMhckAHAiAEoEQIcAFGLmEcGPTxsZHBd98ufI4M6seasOCBIaMxckAHAiAEoEQIcAFGTe5aMPGjNxSXQ6HoC0ebMCAoCCEAAlAqBDALwgAHAiAEoEQIcAeEEAoCnIcQAAFURJREFU4EQAlAiADgHwggDAiQAoEQAdAuAFAYATAVAiADoEwAsCACcCoEQAdAiAFwQATgRAiQDoEAAvCACcCIASAdAhAF4QADgRACUCoEMAvCAAcCIASgRAhwB4QQDgRACUCIAOAfCCAMCJACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8IIAwIkAKBEAHQLgBQGAEwFQIgA6BMALAgAnAqBEAHQIgBcEAE4EQIkA6BAALwgAnAiAEgHQIQBeFCMAC1ta\/lWs8RQNAdAhAEoEQIcAeFGMAJQlAqBDAJQIgA4B8IIAwIkAKBEAHQLgBQGAEwFQIgA6BMALAgAnAqBEAHQIgBcEAE4EQIkA6BAALwgAnAiAEgHQIQBeEAA4EQAlAqBDALwgAHAiAEoEQIcAeEEA4EQAlAiADgHwggDAiQAoEQAdAuAFAYATAVAiADoEwAsCACcCoEQAdAiAFwQATgRAiQDoEAAvCACcCIASAdAhAF4QADgRACUCoEMAvCAAcCIASgRAhwB4QQDgRACUCIAOAfCCAMCJACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8IIAwIkAKBEAHQLgBQGAEwFQIgA6BMALAgAnAqBEAHQIgBcEAE4EQIkA6BAALwgAnAiAEgHQIQBeEAA4EQAlAqBDALwgAHAiAEoEQIcAeEEA4EQAlAiADgHwggDAiQAoEQAdAuAFAYATAVAiADoEwAsCACcCoEQAdAiAFwQATgRAiQDoEAAvCACcCIASAdAhAF4QADgRACUCoEMAvCAAcCIASgRAhwB4QQDgRACUCIAOAfCCAMCJACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8OLhFzsUjhW5XbN8H3qv1AOocLPZgDrvzSr1CCrc\/0o9gD41e9K0sggAvwHAid8AlPgNQIffALwgAHAiAEoEQIcAeEEA4EQAlAiADgHwggDAiQAoEQAdAuAFAYATAVAiADoEwAsCACcCoEQAdAiAFwQATgRAiQDoEAAvCACcCIASAdAhAF4QADgRACUCoEMAvCAAcCIASgRAhwB4QQDgRACUCIAOAfCCAMCJACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8IIAwIkAKBEAHQLgBQGAEwFQIgA6BMALAgAnAqBEAHQIgBcEAE4EQIkA6BAALwgAnAiAEgHQIQBeEAA4EQAlAqBDALwgAHAiAEoEQIcAeEEA4EQAlAiADgHwggDAiQAoEQAdAuAFAYATAVAiADoEwAsCACcCoEQAdAiAFwQATgRAiQDoEAAvCACcCIASAdAhAF4QADgRACUCoEMAvNAF4JK6umeKNZIiIwA6BECJAOgQAC90AShjBECHACgRAB0C4AUBgBMBUCIAOgTACwIAJwKgRAB0CIAXBABOBECJAOgQAC8IAJwIgBIB0CEAXhAAOBEAJQKgQwC8IABwIgBKBECHAHhBAOBEAJQIgA4B8IIAwIkAKBEAHQLgBQGAEwFQIgA6BMALAgAnAqBEAHQIgBcEAE4EQIkA6BAALwgAnAiAEgHQIQBeEAA4EQAlAqBDALwgAHAiAEoEQIcAeEEA4EQAlAiADgHwggDAiQAoEQAdAuAFAYATAVAiADoEwAsCACcCoEQAdAiAFwQATgRAiQDoEAAvCACcCIASAdAhAF4QADgRACUCoEMAvCAAcCIASgRAhwB4QQDgRACUCIAOAfCCAMCJACgRAB0C4AUBgBMBUCIAOgTAi0f+PW3tNOXtUo+gsr01pdQjqHAvv1nqEVS2t18q9Qj61FtlEoDHHllLPVzqAXTj4bIfYKkHUOHYfkpr+wacURYB6K05kxaVegiV7d\/PlXoElW3BpHmlHkJle\/HpUo+gsi2dNKuo6yMA1YUA6BAAJQKgQwBKPYTKRgB0CIASAdAhAKUeQmUjADoEQIkA6BCAUg+hshEAHQKgRAB0CECph1DZCIAOAVAiADoEoNRDqGwEQIcAKBEAnSoPwIo5q0o9hMo2f0GpR1DZVs7hnbYqCz8q9Qgq2+o5y4u6vgoLAACgWAgAAFQpAgAAVYoAAECVIgAAUKUqKgBLrxnTcMbtvA6oS7cHce+HUxmbrIcTVe2xhsTL7Hqz6diOye3HftgL71569MHHjns9NuFlB6ykAMw5KQiOCIIzF5Z6IGXtsvQ7XsYm6+FEdTsjiB\/AerPp2I6p7cd+WLinDgyCMSODEbeEE352wEoKwFnBGXPMBycEl5Z6IGXtjOCtpZE1JmuT9XCimi2ZGCQOYL3ZdFW\/HdO2H\/thweaPDC6eZ1bdM2LEFONrB6ygAEwNDgvDNvvAER+WeijlbHSwNHk6Y5P1cKKKtZ52SJA4gPVm01X7dkzffuyHhbsjOD168mZicIG3HbCCAjAx+EP0\/czg\/hKPpJwtH\/HT1ETGJuvhRBW75vDDDx8RP4D1ZtNV+3ZM337sh4W7MHgo+j41OMrbDlhBATgrmBx9v6Naf0PskfeC01ITGZushxNV7vD4Aaw3m47tmNp+7IeFG3fUy9H3acEobztgBQVgdPBK9H1ycGqJR1LOngsuuHNsw88ubg8nMjZZDyeqXOIA1ptNx3ZMbT\/2w967Jfittx2wggLQEEyPvj8fnFDikZSz+4MgOOjIIDjwPpO1yXo4UeUSB7DebDq2Y2r7sR\/22ksHBy972wErKAAHBh3R99eCMSUeSTn7S3DYsyvN4muDA97O2mQ9nKhyiQNYbzYd2zG1\/dgPe2nZjQcEtxtvO2AFBeCnybydUuKRlLN3XpgTfb8kfCVBxibr4USVSxzAerPp2I6p7cd+2DtPHxU0PBKe8LQDVlAATg5ejb5PDp8hQzdeDh8EZGyyHk5UucQBrDebju2Y2n4J7IeFWNAcBBfFHsV72gErKADnBE9G3+8OrijxSCpBR3DQmsxN1sOJKpc4gPVm07EdcwPAfliAjtHBiVPjpz3tgBUUgInBn6Lv5wQPlngkZWzJ\/Q+siU68Fpyctcl6OFHlDk++D6DwTcd2TG4\/9sNeWHJ8MD75D+c87YAVFIBXglHL7LcFB49cXOqhlK81RwRt0Ykrgj9nbbIeTlS5RAB6s+nYjsntx37YC\/elP3PjaQesoACYU4OLV5llZwbjSz2QcnZbcMRLxiy9ecRPwn++mrHJejhR3ZJPYfRm07Edk9uP\/bBwJwZTVscZXztgJQVg5hHBj08bGRxXrZ8V2COrrguCn4wZEfwkegCWscl6OFHdkgHozaZjOya3H\/thwVYdkPgA1aDR+NoBKykAZt7low8aM3FJqYdR5p77zU8afjlhfmwiY5P1cKKqpf6I2ZtNx3ZMbT\/2wwLNCjIC4GcHrKgAAACKhwAAQJUiAABQpQgAAFQpAgAAVYoAAECVIgAAUKUIAABUKQIAAFWKAABAlSIAAFClCAAAVCkCgDI266z9dxq8ya5HPrxStZpVIr\/v+bnnilxb0Or\/LI8WOKAiG7XhrO7PBOQiAChbK09fT+J2n6ZZUd8G4INPfLvQARXZO+s2lHgEqFAEAGXrFyKDRl92x5WnbiVS855iRX0bgJHy70IHVGwnyv2lHgIqEgFAuZoksuvM6NTyc0X2UaypsACsmT374wJW\/rTUFzygYpvRf7s1pR4DKhEBQLn6gcj0xOlDRNp6v6bCAlCgBrm5z9bdY\/VSnf9HHUoEAOVqS9khefpukT\/lO9\/Cpe75i1YlTvVlAGYMGJb2H7hTl5nHqo7uVtj9ORyXdZN8t4dLAWkIAMrVZrJl8nTHGWc8EZ147Li9Nh220\/73ro6mhskDL3+9nwzb\/XpjFpyx\/ZDN9o4eCF8hu5gbthPZeJ\/ro\/OlArD44j2GD9v1+DeyLmr+eXttsd7nDn46dp7obwDnS9KArhY9W44wrsvMWWKAPGLu3G3A1ekL\/\/tHnxq8XdOiN0RmZ51jxtgdhw7bceyb0dlmi9wZW+De6Lei3MtavJ682dPtCiQRAJSrb4n8IWvWisMSB+X9ooe\/w+SCYbHps97dJnbichMdIM+Jn2+vBSYtAP\/dMja33+8yVvt0Tfzcvwin3AHIs6j5glwffc++zJwl7OH9CjuVHoBL+kdn+PzDyQAkznHv8Niy614Wns8RgKzLMnvLpb3ZxqhyBADl6gZ7fPv2bYvSZzWLbH\/JPXc3by\/yx3B6mKwjhz3wzK\/XlX6fkmP+\/uRJ\/WT9ReEBckg\/2X3cX0\/5lMiBJhWAD+2B\/scT\/nbaJ0QuTFvrrE\/K0JNuu+\/CLUQmmUQA5r4Rc0M\/+X7+Rc3cfjI1OpF9mTlLDJBz1tn8dy0zU8vao\/mWZ995wS4yOBGAxDnuExnYeP0NjQNF7jGuAGRdljFnyP5F2eioLgQA5WrN4dFj4G+e88TyxKytZceF4fcF28iI8Lt9+H9++H2cxP9GcILIc+EBUmRM+N6xD+pE\/pMKwM9lwL3h93d3kCHTUxc0UaQ1\/N4+UI4yiQDEdWwh28zLv6hpkQ1jr7\/JvsycJQbIgH3nZ1y\/XWSn9+33RfWSCED8HKt2lI2fCU88PVw+t8oVgKzLCn+wvu7NcqhKBADl68avxp7nWO\/7t68Ip5dttWX8T8GjZZfw2zDZPnoqaKrIF6Lj8JMiLdEB8tPRAuatdWS\/ZADeHSi\/jC3+mMivUhfzcxkYO4ifPXqcyQzAyr1l8AtdLGp+Kd+Mnci+zJwlBkj\/VzOuXUvsFw67QL9EAOLnuCH2TJb1B5G\/OgOQcVnWOyJTut+gQCYCgHL2zrWHbRo1YOsXM+b\/MBGAY6PJhRI\/2trj4O3RAfKP8TMeKkPWJAJwpciM+OztZO\/Uyi4Q+UvautMD8AuRiaaLRc1P5KDYiezLzFligHwr87qNka3if8KtTwQgfo5jZcP4OxEWDZPjnQHIuKzYFnjEAAUiACh3r43fs7\/IkFfik2vef+ovIyURgLOjeYsTrxKdngjAU\/FzjxeZmQjAKbJFYp0HyMapC3hrqMju5z+9LD6ZFoDbRRqjE\/kWNd+WMbET2ZeZs8QAOTLzeu2VfOnmyYkAxM+xj+yRONMX5TvOAGRcVmig3GCAAhEAVIDXvi6x99u+Ona3IbGnhbIDcEV0IhmAxKPvSeGxMh6A\/SVN\/7TVt342nDOo\/qoF8ZXFA\/DKUNk9loW8i+4sp8VOZF9mzhID4mNN2kKOjp+6JBGA+Dm2kkMTZzpIap0ByLis0KekuUebEkhDAFCmnr857R22C7aW\/kuNuWgdkYFf+vFv\/n50dwF4P77gIyJ\/TwTgy+nHZEn\/m+myu0ZvFs7b\/J8mLQDzt5ON4n\/wzbvo7nJy7ET2ZeYsMSD29+qU4YlfHsyliQDEz7GVHJY4U4Ns7gxAxmXF1ja++20KZCIAKFPjEk9uRI4XeT38c+ouk5bFJrsJwNPx5ezJdxMB+JF8rYvLe+ua\/UQ2npsKwJoR0v\/v8R\/mXXT\/+PvAci4zZ4mcAHxFvhc\/9YusAOyTWnZ32Ss9ANclApBxWdbq\/vK3Lq4c4EQAUKZuTh71QiNEOsxPZcjs2OQx3QUg8XB4lAxcnQjAr2XTxEemrVq1OrXu6dPnxU5cLnJ3KgDniVyQOEu+Re1AYq\/CybnMnCVyAnC4fDp+jv2yAnCsbBj\/eIuPNwj\/zG0DcEdsuikRgIzLMtFHmD5hgAIRAJSp9wfJdslP+Zm5gewcPjLeOTa55gvdBeAzsadp2tcNX1kTD8C9IvfGlp+zfvprObdIvIlqqsh1yQA83F+C5Eds5lvUnC+7xk5kX2bOEjkBuFTkoejEO+tkBeC6xNUxl0VjsQGIvc93+WcSAci4LGuKyP+62JiAEwFAuTpR5HPPx07O+KKEz3AcK4OjT0pban+0U3gifwDk+PANArP3EJmcDMCaL8vwx8NzLQ+kX9p\/mAlkcOwlRmeEzzPFA\/DOcNk29b6tfIuap2Wd2GfBZV9mzhI5AfjoE7JLeNxf8n3JCsDKHWST6P1dz24k29sj\/ephUhce8NecJsk3gqVflnWNfKbQ7QsQAJSthfboNuAH515xWdMP7UPkU+2ce0R2vm3K5PG1MlT6P7KwqwAMkT2abz9tC5EfmNQ7gZ\/pJ+uOuuqBS3fMfDPXJJFBP7\/p4Wv3E\/nOmngAVnxB1rl7WtyCvIualetLdJjPucycJXICYG4R2WbcfZfsJl8V6cw4h72ig8fecsuJg+K\/RvxQ5Fv3vXTb96V\/IgCZlxW+qeDYomx0VBcCgLI1\/+h+yddR\/jx6x++Y+NTpLRJ9Ck7+ADy8YeysRywxaR8G9+gWidVlXFBT4nL2mG\/iAZid\/hqea\/Mvao\/Nsc8Gyr7MnCVyA2BuGhydYex4kWWZ57h0QGzZ4fdEk+9tEh\/JxYkAZF2W2Tm83kCBCADK2Mu\/+doWgzauO\/i8xLMu99RvNWibMS+b1cdtOOSkrgKw+IOf1w4avtdfo7mpj4PuPP3zG6z\/pdGvZl3Oi4d\/cfiwzx\/0YGJljgDkW\/Tm+Ct2si8zZwlHAMwLI4YP\/crN5mwZnH2ON07YfujQHcYmXu0575e7DRX5xI1zEwHIuqxpssEiAxSKAGDtEx4gfV3W8s3kbe1lHhv7k3Y3Zry0In4q97LOkVN6e+GoZgQAax+fATDnym96d5n37rLLB9GJVVvIqIKWzL2sz\/af5j4r0BUCgLWP1wDMHvbp5b26zHaRi6MTN4jcV9CSOZf1qDQUdtlAhABg7eM1AOZP0d8fenGZP5SBF7zx4TNn9ZfdV3d\/7jQ5l7XH+jPynBXoCgHA2sdvAFZ\/ZbMlvbrM+bvF\/8i8\/TuFLZh9Wfcl3jcGFIYAYO1zb3390u7PVTSvHPNM7y5z1XX7bj1w0+\/8eVn3Z82QfVl\/\/GVhv0EAcQQAAKoUAQCAKkUAAKBKEQAAqFIEAACqFAEAgCpFAACgShEAAKhSBAAAqhQBAIAqRQAAoEoRAACoUgQAAKoUAQCAKkUAAKBKEQAAqFL\/D6wpc88N7TsAAAAAAElFTkSuQmCC\" width=\"768\"\/><\/p>\n<\/figure>\n<\/div>\n<\/div>\n<\/div>\nBut in the spirit of focusing on \u201cerror rates,\u201d let\u2019s explore some other error types - meet Type S and Type M error rates.<\/p>\n<section class=\"level3\" id=\"type-m-and-s-errors\">\n<h3 data-anchor-id=\"type-m-and-s-errors\">Type M and S errors<\/h3>\n<a href=\"https:\/\/journals.sagepub.com\/doi\/10.1177\/1745691614551642\">Gelman and Carlin<\/a> (2014) introduced two other metrics to use in statistical inference:<\/p>\n<ul>\n<li>Type S (sign) error.<\/strong> It\u2019s the probability that an observed difference, when declared statistically significant, has an opposite sign than the actual difference. In other words, it is the probability that you will make a decision that is opposite to what you are after. It directly relates to the practical implications of making a wrong decision. In a lot of situations, rolling out a zero-impact change is not the end of the world. Rolling out something that has an impact opposite to the one estimated in an experiment? That\u2019s bad!<\/li>\n<li>Type M (Magnitude) error<\/strong> measures how much the observed difference, when declared statistically significant, differs from the actual difference, expressed as a ratio. It partially addresses the issue of quantifying the risk of saying \u201ceffect sizes so small that they don\u2019t practically matter\u201d as statistically significant and is closely related to the <a href=\"https:\/\/www.etsy.com\/codeascraft\/mitigating-the-winners-curse-in-online-experiments?utm_source=pocket_reader\">winner\u2019s curse phenomenon<\/a>. I like Type M error a lot because it provides a rigorous quantification of the hand-wavy statement, \u201cYeah, the results are statistically significant, but I would not trust them due to the small sample size.\u201d It provides a bridge between sample size and Type I error rate.<\/li>\n<\/ul>\n<section class=\"level4\" id=\"calculating-type-s-and-m-error-rates\">\n<h4 data-anchor-id=\"calculating-type-s-and-m-error-rates\">Calculating Type S and M error rates<\/h4>\nThe original paper proposed calculating Type S and M error rates via simulation. using a function. In 2019, <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/29569719\/\">Lu, Qui, and Deng<\/a> published a paper that included closed-form formulas for these error rates. Their implementation is below (and can be easily extended to unequal sample size designs).<\/p>\nThe rates are a function of four parameters: <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>A<\/mi><annotation encoding=\"application\/x-tex\">A<\/annotation><\/semantics><\/math>, the effect size; <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03c3<\/mi><annotation encoding=\"application\/x-tex\">\\sigma<\/annotation><\/semantics><\/math>, the observed standard deviation of a metric; <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>\u03b1<\/mi><annotation encoding=\"application\/x-tex\">\\alpha<\/annotation><\/semantics><\/math>, the chosen level of statistical significance; and <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>n<\/mi><annotation encoding=\"application\/x-tex\">n<\/annotation><\/semantics><\/math>, the sample size.<\/p>\n<math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>A<\/mi><annotation encoding=\"application\/x-tex\">A<\/annotation><\/semantics><\/math>, conceptually, is a hypothesized<\/em> effect size. That\u2019s not the same as <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>M<\/mi><mi>D<\/mi><mi>E<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">MDE<\/annotation><\/semantics><\/math> - arguably, in a well-designed experiment, <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>M<\/mi><mi>D<\/mi><mi>E<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">MDE<\/annotation><\/semantics><\/math> should always be smaller than <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>A<\/mi><annotation encoding=\"application\/x-tex\">A<\/annotation><\/semantics><\/math>, as it\u2019s the minimum effect size you\u2019re interested in detecting.<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\" open=\"\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb10\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb10-1\" tabindex=\"-1\"><\/a>calculate_type_SM =<\/span> function<\/span>(A, sd, n, alpha =<\/span> 0.05<\/span>) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-2\" tabindex=\"-1\"><\/a> # A - effect size<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-3\" tabindex=\"-1\"><\/a> # sd - baseline standard deviation<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-4\" tabindex=\"-1\"><\/a> # n - size per group<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-5\" tabindex=\"-1\"><\/a> lambda =<\/span> abs<\/span>(A) \/<\/span> sqrt<\/span>(sd**<\/span>2<\/span>\/<\/span>n +<\/span> sd**<\/span>2<\/span>\/<\/span>n)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-6\" tabindex=\"-1\"><\/a> z =<\/span> qnorm<\/span>(1<\/span> -<\/span> alpha\/<\/span>2<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-7\" tabindex=\"-1\"><\/a> neg =<\/span> pnorm<\/span>(-<\/span>z -<\/span>lambda)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-8\" tabindex=\"-1\"><\/a> diff =<\/span> pnorm<\/span>(z -<\/span> lambda)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-9\" tabindex=\"-1\"><\/a> pos =<\/span> pnorm<\/span>(z +<\/span> lambda)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-10\" tabindex=\"-1\"><\/a> inv_diff =<\/span> pnorm<\/span>(lambda -<\/span> z)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-11\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-12\" tabindex=\"-1\"><\/a> power =<\/span> neg +<\/span> 1<\/span> -<\/span> diff,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-13\" tabindex=\"-1\"><\/a> typeS =<\/span> neg \/<\/span> (neg +<\/span> 1<\/span> -<\/span> diff),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-14\" tabindex=\"-1\"><\/a> typeM =<\/span> (<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-15\" tabindex=\"-1\"><\/a> dnorm<\/span>(lambda +<\/span> z) +<\/span> dnorm<\/span>(lambda -<\/span> z) +<\/span> lambda *<\/span> (pos +<\/span> inv_diff -<\/span> 1<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-16\" tabindex=\"-1\"><\/a> ) \/<\/span> (lambda *<\/span> (1<\/span> -<\/span> pos +<\/span> inv_diff))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-17\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb10-18\" tabindex=\"-1\"><\/a>}<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<\/div>\nTo get a sense of how these error rates look like, let\u2019s use the setup from the examples above: a baseline of 17%,<\/em> the hypothesized effect size of 1 percentage point, and a sample size of 10,000 users in each group:<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\" open=\"\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb11\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb11-1\" tabindex=\"-1\"><\/a>closed_form =<\/span> calculate_type_SM<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb11-2\" tabindex=\"-1\"><\/a> A=<\/span>0.01<\/span>, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb11-3\" tabindex=\"-1\"><\/a> sd=<\/span>sqrt<\/span>(baseline *<\/span> (1<\/span> -<\/span> baseline)), <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb11-4\" tabindex=\"-1\"><\/a> n =<\/span> required_sample_size<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb11-5\" tabindex=\"-1\"><\/a>) |><\/span> as_tibble<\/span>() |><\/span> mutate<\/span>(method =<\/span> 'Closed-form formula'<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb11-6\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb11-7\" tabindex=\"-1\"><\/a>closed_form |><\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb11-8\" tabindex=\"-1\"><\/a> kbl<\/span>(caption =<\/span> 'Estimated Metrics'<\/span>, digits=<\/span>6<\/span>) |><\/span> kable_styling<\/span>()<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<table class=\"table table-sm table-striped small\" data-quarto-postprocess=\"true\">\n<caption>Estimated Metrics<\/caption>\n<thead>\n<tr class=\"header\">\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">power<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">typeS<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">typeM<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: left;\">method<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td style=\"text-align: right;\">0.469165<\/td>\n<td style=\"text-align: right;\">0.00013<\/td>\n<td style=\"text-align: right;\">1.45038<\/td>\n<td style=\"text-align: left;\">Closed-form formula<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\nThe risk of a sign error in such a setting is very small - just <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mo>\u223c<\/mo><mn>1<\/mn><mi>\/<\/mi><mn>7700<\/mn><\/mrow><annotation encoding=\"application\/x-tex\">\\sim1\/7700<\/annotation><\/semantics><\/math>. The type M ratio, on the other hand, is <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mn>1.45<\/mn><annotation encoding=\"application\/x-tex\">1.45<\/annotation><\/semantics><\/math>; a 45% overestimation can be meaningful in decision-making.<\/p>\nWe can verify that these formulas are correct with a small simulation. In this simulation, we repeat the same experiment with the above parameters many times and estimate empirical error rate estimates. Note that estimating Type S error accurately via simulation isn\u2019t easy given how rare it is, which is why even with 100,000 loops, we are still a bit off.<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\" open=\"\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb12\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb12-1\" tabindex=\"-1\"><\/a>pboptions<\/span>(type=<\/span>\"none\"<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-2\" tabindex=\"-1\"><\/a>sim_results =<\/span> pbsapply<\/span>(1<\/span>:<\/span>100000<\/span>, function<\/span>(i) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-3\" tabindex=\"-1\"><\/a> set.seed<\/span>(i*<\/span>24<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-4\" tabindex=\"-1\"><\/a> A =<\/span> rbinom<\/span>(required_sample_size, 1<\/span>, baseline)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-5\" tabindex=\"-1\"><\/a> B =<\/span> rbinom<\/span>(required_sample_size, 1<\/span>, baseline +<\/span> 0.01<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-6\" tabindex=\"-1\"><\/a> mean_diff =<\/span> mean<\/span>(B) -<\/span> mean<\/span>(A)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-7\" tabindex=\"-1\"><\/a> p_value =<\/span> t.test<\/span>(B, A)$<\/span>p.value <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-8\" tabindex=\"-1\"><\/a> c<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-9\" tabindex=\"-1\"><\/a> detected =<\/span> p_value <=<\/span> 0.05<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-10\" tabindex=\"-1\"><\/a> sign_error =<\/span> ifelse<\/span>(p_value <=<\/span> 0.05<\/span>, sign<\/span>(mean_diff) !=<\/span> sign<\/span>(0.01<\/span>), NA<\/span>),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-11\" tabindex=\"-1\"><\/a> ratio =<\/span> ifelse<\/span>(p_value <=<\/span> 0.05<\/span>, mean_diff \/<\/span> 0.01<\/span>, NA<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-12\" tabindex=\"-1\"><\/a> ) <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-13\" tabindex=\"-1\"><\/a>}, cl=<\/span>4<\/span>) |><\/span> t<\/span>() |><\/span> as_tibble<\/span>()<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-14\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-15\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-16\" tabindex=\"-1\"><\/a>empirical_rates =<\/span> sim_results |><\/span> summarize<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-17\" tabindex=\"-1\"><\/a> power =<\/span> mean<\/span>(detected, na.rm=<\/span>T),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-18\" tabindex=\"-1\"><\/a> typeS =<\/span> mean<\/span>(sign_error, na.rm=<\/span>T),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-19\" tabindex=\"-1\"><\/a> typeM =<\/span> mean<\/span>(ratio, na.rm=<\/span>T),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-20\" tabindex=\"-1\"><\/a> method =<\/span> 'Empirical results'<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-21\" tabindex=\"-1\"><\/a>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-22\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-23\" tabindex=\"-1\"><\/a>bind_rows<\/span>(closed_form, empirical_rates) |><\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb12-24\" tabindex=\"-1\"><\/a> kbl<\/span>(caption =<\/span> 'Estimated Metrics'<\/span>) |><\/span> kable_styling<\/span>()<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<table class=\"table table-sm table-striped small\" data-quarto-postprocess=\"true\">\n<caption>Estimated Metrics<\/caption>\n<thead>\n<tr class=\"header\">\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">power<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">typeS<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">typeM<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: left;\">method<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td style=\"text-align: right;\">0.4691649<\/td>\n<td style=\"text-align: right;\">0.0001298<\/td>\n<td style=\"text-align: right;\">1.450380<\/td>\n<td style=\"text-align: left;\">Closed-form formula<\/td>\n<\/tr>\n<tr class=\"even\">\n<td style=\"text-align: right;\">0.4581100<\/td>\n<td style=\"text-align: right;\">0.0000655<\/td>\n<td style=\"text-align: right;\">1.462017<\/td>\n<td style=\"text-align: left;\">Empirical results<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\nLu, Qiu, and Deng\u2019s paper includes charts that perfectly illustrate the risk associated with underpowered experiments. On the x-axis, we plot statistical power (or, equivalently, sample size given a chosen <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>A<\/mi><mo>=<\/mo><mi>M<\/mi><mi>D<\/mi><mi>E<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">A=MDE<\/annotation><\/semantics><\/math>), and on the y-axis - Type S error rate \/ Type M ratio:<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb13\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb13-1\" tabindex=\"-1\"><\/a>error_rates =<\/span> lapply<\/span>(seq<\/span>(500<\/span>, 40000<\/span>, 500<\/span>), function<\/span>(n) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-2\" tabindex=\"-1\"><\/a> calculate_type_SM<\/span>(0.01<\/span>, sqrt<\/span>(baseline *<\/span> (1<\/span> -<\/span> baseline)), n)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-3\" tabindex=\"-1\"><\/a>}) |><\/span> bind_rows<\/span>() |><\/span> pivot_longer<\/span>(-<\/span>power)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-4\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-5\" tabindex=\"-1\"><\/a>ggplot<\/span>(error_rates, aes<\/span>(x=<\/span>power)) +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-6\" tabindex=\"-1\"><\/a> geom_line<\/span>(aes<\/span>(y=<\/span>value, color=<\/span>name)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-7\" tabindex=\"-1\"><\/a> scale_x_continuous<\/span>(breaks=<\/span>seq<\/span>(0.05<\/span>, 0.95<\/span>, 0.2<\/span>)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-8\" tabindex=\"-1\"><\/a> facet_wrap<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-9\" tabindex=\"-1\"><\/a> ~<\/span>name, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-10\" tabindex=\"-1\"><\/a> scales=<\/span>'free_y'<\/span>, strip.position =<\/span> 'top'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-11\" tabindex=\"-1\"><\/a> labeller =<\/span> as_labeller<\/span>(c<\/span>(typeM =<\/span> 'Type M error ratio'<\/span>, typeS=<\/span>'Type S error rate'<\/span>))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-12\" tabindex=\"-1\"><\/a> ) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-13\" tabindex=\"-1\"><\/a> labs<\/span>(x =<\/span> 'Statistical power'<\/span>) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-14\" tabindex=\"-1\"><\/a> theme_light<\/span>() +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-15\" tabindex=\"-1\"><\/a> theme<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-16\" tabindex=\"-1\"><\/a> panel.grid.major.x =<\/span> element_blank<\/span>(),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-17\" tabindex=\"-1\"><\/a> legend.position =<\/span> \"none\"<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-18\" tabindex=\"-1\"><\/a> strip.placement =<\/span> \"outside\"<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb13-19\" tabindex=\"-1\"><\/a> ) +<\/span> ylab<\/span>(NULL<\/span>)<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<div>\n<figure class=\"figure\">\n<img decoding=\"async\" class=\"img-fluid figure-img\" 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width=\"768\"\/><\/p>\n<\/figure>\n<\/div>\n<\/div>\n<\/div>\nWe can see that Type S error (which is very costly, decision-making-wise!) largely disappears once we hit the statistical power of 0.35\u22120.4. On the other hand, the type M ratio only goes below 1.5x once we achieve the statistical power of <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mn>0.5<\/mn><annotation encoding=\"application\/x-tex\">0.5<\/annotation><\/semantics><\/math>.<\/p>\nThese charts should be a part of any Experimentation 101 course.<\/p>\n<\/section>\n<\/section>\n<section class=\"level3\" id=\"obtaining-long-term-sign-error-exaggeration-rates\">\n<h3 data-anchor-id=\"obtaining-long-term-sign-error-exaggeration-rates\">Obtaining long-term sign-error \/ exaggeration rates<\/h3>\nType M and S rates depend on a hypothesized effect size <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mi>A<\/mi><annotation encoding=\"application\/x-tex\">A<\/annotation><\/semantics><\/math>, just like the Type II error rate depends on <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>M<\/mi><mi>D<\/mi><mi>E<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">MDE<\/annotation><\/semantics><\/math>, so we\u2019ll need the same integration approach to get to long-term \u201caverage\u201d rates. We\u2019ll also tweak the definitions a bit and calculate:<\/p>\n<ul>\n<li>Expected \u201csign error rate\u201d - the % of experiments where we will declare the results statistically significant, but the observed treatment effect will have an opposite sign than the actual treatment effect. We\u2019ll use all experiments as the denominator here, which is slightly different from the Type S error rate definition. If needed, we can divide this rate by the average detection rate to get back to the original definition.<\/p><\/li>\n<li>Expected \u201caverage absolute exaggeration\u201d - the expected absolute difference between the observed and actual treatment effects among experiments that we declare statistically significant. We\u2019ll focus on absolute difference rather than a Type M-style ratio metric because integrating Type M ratio directly at small effect sizes is challenging as the ratio\u2019s denominator approaches zero.<\/p><\/li>\n<\/ul>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb14\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb14-1\" tabindex=\"-1\"><\/a>estimate_sign_error_rate =<\/span> function<\/span>(baseline, s, density_func) { <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-2\" tabindex=\"-1\"><\/a> baseline_sd =<\/span> sqrt<\/span>(baseline *<\/span> (1<\/span> -<\/span> baseline))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-3\" tabindex=\"-1\"><\/a> integrate<\/span>(function<\/span>(x) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-4\" tabindex=\"-1\"><\/a> r =<\/span> calculate_type_SM<\/span>(n=<\/span>s, A=<\/span>x, sd=<\/span>baseline_sd)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-5\" tabindex=\"-1\"><\/a> (r$<\/span>typeS *<\/span> r$<\/span>power) *<\/span> density_func<\/span>(x) <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-6\" tabindex=\"-1\"><\/a> }, -<\/span>Inf<\/span>, Inf<\/span>, stop.on.error =<\/span> F)$<\/span>value<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-7\" tabindex=\"-1\"><\/a>}<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-8\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-9\" tabindex=\"-1\"><\/a>estimate_abs_exaggeration =<\/span> function<\/span>(baseline, s, density_func) { <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-10\" tabindex=\"-1\"><\/a> baseline_sd =<\/span> sqrt<\/span>(baseline *<\/span> (1<\/span> -<\/span> baseline))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-11\" tabindex=\"-1\"><\/a> integrate<\/span>(function<\/span>(x) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-12\" tabindex=\"-1\"><\/a> r =<\/span> calculate_type_SM<\/span>(n=<\/span>s, A=<\/span>x, sd=<\/span>baseline_sd)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-13\" tabindex=\"-1\"><\/a> (r$<\/span>typeM -<\/span> 1<\/span>) *<\/span> density_func<\/span>(x) *<\/span> abs<\/span>(x) *<\/span> r$<\/span>power <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-14\" tabindex=\"-1\"><\/a> }, -<\/span>Inf<\/span>, Inf<\/span>, stop.on.error =<\/span> F)$<\/span>value<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-15\" tabindex=\"-1\"><\/a>}<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-16\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-17\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-18\" tabindex=\"-1\"><\/a>sign_and_ratio_rates =<\/span> pblapply<\/span>(sample_sizes, function<\/span>(n) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-19\" tabindex=\"-1\"><\/a> lapply<\/span>(eff_size_distrs, function<\/span>(d) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-20\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-21\" tabindex=\"-1\"><\/a> name =<\/span> d$<\/span>name, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-22\" tabindex=\"-1\"><\/a> sign_error_rate =<\/span> estimate_sign_error_rate<\/span>(baseline, n, d$<\/span>density),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-23\" tabindex=\"-1\"><\/a> exaggeration_ratio =<\/span> estimate_abs_exaggeration<\/span>(baseline, n, d$<\/span>density), <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-24\" tabindex=\"-1\"><\/a> n=<\/span>n<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-25\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-26\" tabindex=\"-1\"><\/a> })<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-27\" tabindex=\"-1\"><\/a> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-28\" tabindex=\"-1\"><\/a>}, cl=<\/span>4<\/span>) |><\/span> bind_rows<\/span>() |><\/span> pivot_longer<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-29\" tabindex=\"-1\"><\/a> cols=<\/span>c<\/span>('sign_error_rate'<\/span>, 'exaggeration_ratio'<\/span>), names_to =<\/span> \"metric\"<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb14-30\" tabindex=\"-1\"><\/a>)<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<\/div>\nHow do these rates look under the assumptions of effect size distributions we used earlier?<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb15\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb15-1\" tabindex=\"-1\"><\/a>ggplot<\/span>(sign_and_ratio_rates) +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-2\" tabindex=\"-1\"><\/a> geom_line<\/span>(aes<\/span>(x=<\/span> n, y=<\/span>value, color=<\/span>name)) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-3\" tabindex=\"-1\"><\/a> theme_light<\/span>() +<\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-4\" tabindex=\"-1\"><\/a> theme<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-5\" tabindex=\"-1\"><\/a> panel.grid.major =<\/span> element_blank<\/span>(),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-6\" tabindex=\"-1\"><\/a> legend.position =<\/span> \"bottom\"<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-7\" tabindex=\"-1\"><\/a> ) +<\/span> labs<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-8\" tabindex=\"-1\"><\/a> x =<\/span> 'Sample size (per group)'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-9\" tabindex=\"-1\"><\/a> y =<\/span> ''<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-10\" tabindex=\"-1\"><\/a> color =<\/span> 'Effect size prior assumption'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-11\" tabindex=\"-1\"><\/a> title =<\/span> 'Avg sign error rates and effect exaggeration size function of sample size'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-12\" tabindex=\"-1\"><\/a> subtitle=<\/span>'Note: Discontinuities in the charts are an artifact of numeric integration...'<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-13\" tabindex=\"-1\"><\/a> ) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-14\" tabindex=\"-1\"><\/a> facet_wrap<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-15\" tabindex=\"-1\"><\/a> ~<\/span>metric, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-16\" tabindex=\"-1\"><\/a> scales=<\/span>'free_y'<\/span>, strip.position =<\/span> 'top'<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-17\" tabindex=\"-1\"><\/a> labeller =<\/span> as_labeller<\/span>(c<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-18\" tabindex=\"-1\"><\/a> exaggeration_ratio =<\/span> 'Avg. absolute exaggeration'<\/span>, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-19\" tabindex=\"-1\"><\/a> sign_error_rate=<\/span>'Avg. sign error rate'<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-20\" tabindex=\"-1\"><\/a> ))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-21\" tabindex=\"-1\"><\/a> ) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-22\" tabindex=\"-1\"><\/a> scale_x_continuous<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-23\" tabindex=\"-1\"><\/a> breaks =<\/span> c<\/span>(1000<\/span>, seq<\/span>(0<\/span>, 200000<\/span>, 5000<\/span>)),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-24\" tabindex=\"-1\"><\/a> labels =<\/span> c<\/span>(\"1,000\"<\/span>, sapply<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-25\" tabindex=\"-1\"><\/a> seq<\/span>(0<\/span>, 200000<\/span>, 5000<\/span>), <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-26\" tabindex=\"-1\"><\/a> function<\/span>(x) if_else<\/span>(x %%<\/span> 40000<\/span> ==<\/span> 0<\/span> &&<\/span> x ><\/span> 0<\/span>, format<\/span>(x, big.mark =<\/span> \",\"<\/span>), \"\"<\/span>))<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-27\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb15-28\" tabindex=\"-1\"><\/a> )<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<div>\n<figure class=\"figure\">\n<img decoding=\"async\" class=\"img-fluid figure-img\" 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width=\"768\"\/><\/p>\n<\/figure>\n<\/div>\n<\/div>\n<\/div>\n<ul>\n<li>Broadly, once you enter 5000+ observations per group territory, sign error rates and average absolute exaggeration become limited. Even at \u201csmall\u201d sample sizes (e.g., 1,000 observations), they are not worrisome, especially in the case of the start-up team.<\/p><\/li>\n<li>The mature team has it worse on both metrics, given the same sample size; its experiments have a higher risk of sign error and a higher exaggeration ratio. This reflects the fact that, when the results are statistically significant, the effect sizes tend to be smaller (and thus at a higher risk of being influenced by a higher sampling error).<\/p><\/li>\n<li>Again, a sample size of 80,000 observations per group seems to be where the mature team starts to see diminishing returns. The average absolute exaggeration decreases below <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mn>0.0005<\/mn><annotation encoding=\"application\/x-tex\">0.0005<\/annotation><\/semantics><\/math> (5 basis points), and the sign error rate goes below <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mn>0.25<\/mn><mi>%<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">0.25\\%<\/annotation><\/semantics><\/math>.<\/p><\/li>\n<\/ul>\n<\/section>\n<\/section>\n<section class=\"level2\" id=\"conclusion\">\n<h2 data-anchor-id=\"conclusion\">Conclusion<\/h2>\nSo, what\u2019s the TLDR?<\/p>\n<ul>\n<li>A low observable win rate of (e.g., <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mn>25<\/mn><mi>%<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">25\\%<\/annotation><\/semantics><\/math>) doesn\u2019t necessarily mean that the team produces winning ideas infrequently. It may simply be that the impact those ideas have tends to be smaller than you can reliably detect.<\/p><\/li>\n<li>The only way to increase observable win\/detection rates is by increasing sample sizes (or reducing the variance of the outcome metric). Choosing a higher MDE will not. If the effect sizes you tend to achieve are ~1ppt, you are artificially reducing your long-term win rate 2x if you run experiments with MDE of 1ppt instead of 0.5ppt.<\/p><\/li>\n<li>Once you are in the 5,000+ sample size territory, the risk of sign errors is minimal. On the other hand, exaggeration ratios tend to remain sizable and should be considered when making decisions.<\/p><\/li>\n<\/ul>\nDoes this mean a team operating in a mature setting should insist on 100,000+ observations in every experiment? It depends on how you answer questions like:<\/p>\n<ul>\n<li>Do small effects truly matter? Perhaps having a 50% chance of detecting a 0.5ppt impact is OK? Maybe a tiny positive effect size is still a failure from a business perspective?<\/p><\/li>\n<li>Do experiment runtimes impede the team\u2019s ability to learn and execute? It\u2019s one thing if you\u2019re just impatient and need to accept that waiting for another four weeks will enable a more precise decision, bringing in <math display=\"inline\" xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><semantics><mrow><mi>$<\/mi><mi>$<\/mi><mi>$<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">\\$\\$\\$<\/annotation><\/semantics><\/math> for years to come. It\u2019s another if you\u2019re blocked by the experiment or run so few experiments that you\u2019re not learning enough.<\/p><\/li>\n<\/ul>\nMost importantly, remember that fiddling with an MDE gives you nothing. Sample size is king. Use CUPED to reduce variance and sequential testing to enable peeking; otherwise, it\u2019s all about the actual changes you test.<\/p>\nAnd, if you want to play around with effect size distrubution assumptions and see corresponding win\/detection\/sign error rates - I made a small <a href=\"https:\/\/ab-testing-long-term-error-rates.streamlit.app\/\">Streamlit app<\/a> just for that.<\/p>\n<\/section>\n<section class=\"level2\" id=\"appendix---dont-trust-the-maths\">\n<h2 data-anchor-id=\"appendix---dont-trust-the-maths\">Appendix - Don\u2019t trust the maths<\/h2>\nIntegration formulas look fancy, but do they work? I ran another simulation to double-check\u2014it looks like it all adds up, with exception of exaggeration ratios that are a bit off. I suspect that differences arise from numerical approximations in integration.<\/p>\n<div class=\"cell\">\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb16\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb16-1\" tabindex=\"-1\"><\/a>num_experiments =<\/span> 100000<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-2\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-3\" tabindex=\"-1\"><\/a>used_sample_size =<\/span> floor<\/span>(power.prop.test<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-4\" tabindex=\"-1\"><\/a> p1=<\/span>baseline, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-5\" tabindex=\"-1\"><\/a> p2=<\/span>baseline +<\/span> 0.015<\/span>,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-6\" tabindex=\"-1\"><\/a> power=<\/span>0.2<\/span>)$<\/span>n)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-7\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-8\" tabindex=\"-1\"><\/a>appendix_sim =<\/span> pblapply<\/span>(eff_size_distrs, function<\/span>(d) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-9\" tabindex=\"-1\"><\/a> drawn_effect_sizes =<\/span> sapply<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-10\" tabindex=\"-1\"><\/a> 1<\/span>:<\/span>num_experiments, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-11\" tabindex=\"-1\"><\/a> function<\/span>(i) d$<\/span>generator<\/span>()<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-12\" tabindex=\"-1\"><\/a> ) <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-13\" tabindex=\"-1\"><\/a> set.seed<\/span>(42<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-14\" tabindex=\"-1\"><\/a> exp_results =<\/span> tibble<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-15\" tabindex=\"-1\"><\/a> eff_size =<\/span> drawn_effect_sizes,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-16\" tabindex=\"-1\"><\/a> converted_A =<\/span> rbinom<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-17\" tabindex=\"-1\"><\/a> num_experiments, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-18\" tabindex=\"-1\"><\/a> used_sample_size, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-19\" tabindex=\"-1\"><\/a> baseline<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-20\" tabindex=\"-1\"><\/a> ),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-21\" tabindex=\"-1\"><\/a> converted_B =<\/span> rbinom<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-22\" tabindex=\"-1\"><\/a> num_experiments, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-23\" tabindex=\"-1\"><\/a> used_sample_size, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-24\" tabindex=\"-1\"><\/a> baseline +<\/span> drawn_effect_sizes<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-25\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-26\" tabindex=\"-1\"><\/a> )|><\/span> mutate<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-27\" tabindex=\"-1\"><\/a> conv_rate_A =<\/span> converted_A \/<\/span> used_sample_size,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-28\" tabindex=\"-1\"><\/a> conv_rate_B =<\/span> converted_B \/<\/span> used_sample_size,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-29\" tabindex=\"-1\"><\/a> ) |><\/span> mutate<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-30\" tabindex=\"-1\"><\/a> mean_diff =<\/span> conv_rate_B -<\/span> conv_rate_A,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-31\" tabindex=\"-1\"><\/a> st_error =<\/span> sqrt<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-32\" tabindex=\"-1\"><\/a> (conv_rate_A *<\/span> (1<\/span> -<\/span> conv_rate_A) \/<\/span> used_sample_size) +<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-33\" tabindex=\"-1\"><\/a> (conv_rate_B *<\/span> (1<\/span> -<\/span> conv_rate_B) \/<\/span> used_sample_size)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-34\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-35\" tabindex=\"-1\"><\/a> ) |><\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-36\" tabindex=\"-1\"><\/a> mutate<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-37\" tabindex=\"-1\"><\/a> is_stat_sig =<\/span> abs<\/span>(mean_diff \/<\/span> st_error) >=<\/span> qnorm<\/span>(0.975<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-38\" tabindex=\"-1\"><\/a> ) |><\/span> mutate<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-39\" tabindex=\"-1\"><\/a> sign_error =<\/span> if_else<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-40\" tabindex=\"-1\"><\/a> is_stat_sig, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-41\" tabindex=\"-1\"><\/a> sign<\/span>(mean_diff) !=<\/span> sign<\/span>(drawn_effect_sizes), <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-42\" tabindex=\"-1\"><\/a> NA<\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-43\" tabindex=\"-1\"><\/a> ),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-44\" tabindex=\"-1\"><\/a> exaggeration =<\/span> if_else<\/span>(is_stat_sig, abs<\/span>(mean_diff -<\/span> drawn_effect_sizes), NA<\/span>)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-45\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-46\" tabindex=\"-1\"><\/a> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-47\" tabindex=\"-1\"><\/a> exp_results |><\/span> summarize<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-48\" tabindex=\"-1\"><\/a> distribution =<\/span> d$<\/span>name,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-49\" tabindex=\"-1\"><\/a> detection_rate =<\/span> mean<\/span>(is_stat_sig),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-50\" tabindex=\"-1\"><\/a> avg_sign_error =<\/span> mean<\/span>(replace_na<\/span>(sign_error, F)),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-51\" tabindex=\"-1\"><\/a> avg_exaggeration =<\/span> mean<\/span>(replace_na<\/span>(exaggeration, 0<\/span>)),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-52\" tabindex=\"-1\"><\/a> avg_TypeS =<\/span> mean<\/span>(sign_error, na.rm=<\/span>T),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-53\" tabindex=\"-1\"><\/a> avg_conditional_exaggeration =<\/span> mean<\/span>(exaggeration, na.rm=<\/span>T)<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-54\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-55\" tabindex=\"-1\"><\/a>}) |><\/span> bind_rows<\/span>()<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-56\" tabindex=\"-1\"><\/a><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-57\" tabindex=\"-1\"><\/a>appendix_sim |><\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-58\" tabindex=\"-1\"><\/a> kbl<\/span>(caption=<\/span>'Simulation results using N = 20% power at MDE of 0.015'<\/span>, digits=<\/span>4<\/span>) |><\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb16-59\" tabindex=\"-1\"><\/a> kable_styling<\/span>()<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<table class=\"table table-sm table-striped small\" data-quarto-postprocess=\"true\">\n<caption>Simulation results using N = 20% power at MDE of 0.015<\/caption>\n<thead>\n<tr class=\"header\">\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: left;\">distribution<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">detection_rate<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_sign_error<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_exaggeration<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_TypeS<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_conditional_exaggeration<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td style=\"text-align: left;\">Startup team<\/td>\n<td style=\"text-align: right;\">0.5033<\/td>\n<td style=\"text-align: right;\">0.0028<\/td>\n<td style=\"text-align: right;\">0.0057<\/td>\n<td style=\"text-align: right;\">0.0056<\/td>\n<td style=\"text-align: right;\">0.0114<\/td>\n<\/tr>\n<tr class=\"even\">\n<td style=\"text-align: left;\">Mature team<\/td>\n<td style=\"text-align: right;\">0.0994<\/td>\n<td style=\"text-align: right;\">0.0125<\/td>\n<td style=\"text-align: right;\">0.0022<\/td>\n<td style=\"text-align: right;\">0.1262<\/td>\n<td style=\"text-align: right;\">0.0225<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<details class=\"code-fold\">\n<summary>Show code<\/summary>\n<div class=\"sourceCode cell-code\" id=\"cb17\"><pre class=\"sourceCode r code-with-copy\"><code class=\"sourceCode r\"><a aria-hidden=\"true\" href=\"#cb17-1\" tabindex=\"-1\"><\/a>lapply<\/span>(eff_size_distrs, function<\/span>(d) {<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-2\" tabindex=\"-1\"><\/a> list<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-3\" tabindex=\"-1\"><\/a> distribution =<\/span> d$<\/span>name,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-4\" tabindex=\"-1\"><\/a> detection_rate =<\/span> estimate_detection_rate<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-5\" tabindex=\"-1\"><\/a> baseline =<\/span> baseline, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-6\" tabindex=\"-1\"><\/a> required_sample_size =<\/span> used_sample_size, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-7\" tabindex=\"-1\"><\/a> density_func =<\/span> d$<\/span>density<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-8\" tabindex=\"-1\"><\/a> ),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-9\" tabindex=\"-1\"><\/a> avg_sign_error =<\/span> estimate_sign_error_rate<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-10\" tabindex=\"-1\"><\/a> baseline =<\/span> baseline, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-11\" tabindex=\"-1\"><\/a> s =<\/span> used_sample_size, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-12\" tabindex=\"-1\"><\/a> density_func =<\/span> d$<\/span>density<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-13\" tabindex=\"-1\"><\/a> ),<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-14\" tabindex=\"-1\"><\/a> avg_exaggeration =<\/span> estimate_abs_exaggeration<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-15\" tabindex=\"-1\"><\/a> baseline =<\/span> baseline, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-16\" tabindex=\"-1\"><\/a> s =<\/span> used_sample_size, <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-17\" tabindex=\"-1\"><\/a> density_func =<\/span> d$<\/span>density<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-18\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-19\" tabindex=\"-1\"><\/a> )<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-20\" tabindex=\"-1\"><\/a>}) |><\/span> bind_rows<\/span>() |><\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-21\" tabindex=\"-1\"><\/a> mutate<\/span>(<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-22\" tabindex=\"-1\"><\/a> avg_TypeS =<\/span> avg_sign_error \/<\/span> detection_rate,<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-23\" tabindex=\"-1\"><\/a> avg_conditional_exaggeration =<\/span> avg_exaggeration \/<\/span> detection_rate<\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-24\" tabindex=\"-1\"><\/a> ) |><\/span><\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-25\" tabindex=\"-1\"><\/a> kbl<\/span>(caption=<\/span>'Integration results with equivalent parameters'<\/span>, digits=<\/span>4<\/span>) |><\/span> <\/span>\n\n<a aria-hidden=\"true\" href=\"#cb17-26\" tabindex=\"-1\"><\/a> kable_styling<\/span>()<\/span><\/code><button class=\"code-copy-button\" title=\"Copy to Clipboard\"><\/i><\/button><\/pre><\/div>\n<\/details>\n<div class=\"cell-output-display\">\n<table class=\"table table-sm table-striped small\" data-quarto-postprocess=\"true\">\n<caption>Integration results with equivalent parameters<\/caption>\n<thead>\n<tr class=\"header\">\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: left;\">distribution<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">detection_rate<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_sign_error<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_exaggeration<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_TypeS<\/th>\n<th data-quarto-table-cell-role=\"th\" style=\"text-align: right;\">avg_conditional_exaggeration<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"odd\">\n<td style=\"text-align: left;\">Startup team<\/td>\n<td style=\"text-align: right;\">0.4998<\/td>\n<td style=\"text-align: right;\">0.0026<\/td>\n<td style=\"text-align: right;\">0.0029<\/td>\n<td style=\"text-align: right;\">0.0052<\/td>\n<td style=\"text-align: right;\">0.0057<\/td>\n<\/tr>\n<tr class=\"even\">\n<td style=\"text-align: left;\">Mature team<\/td>\n<td style=\"text-align: right;\">0.0864<\/td>\n<td style=\"text-align: right;\">0.0118<\/td>\n<td style=\"text-align: right;\">0.0021<\/td>\n<td style=\"text-align: right;\">0.1364<\/td>\n<td style=\"text-align: right;\">0.0247<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/section>\n<script id=\"quarto-html-after-body\" type=\"application\/javascript\">\n\nwindow.document.addEventListener(\"DOMContentLoaded\", function (event) {\n\n const toggleBodyColorMode = (bsSheetEl) => {\n\n const mode = bsSheetEl.getAttribute(\"data-mode\");\n\n const bodyEl = window.document.querySelector(\"body\");\n\n if (mode === \"dark\") {\n\n bodyEl.classList.add(\"quarto-dark\");\n\n bodyEl.classList.remove(\"quarto-light\");\n\n } else {\n\n bodyEl.classList.add(\"quarto-light\");\n\n bodyEl.classList.remove(\"quarto-dark\");\n\n }\n\n }\n\n const toggleBodyColorPrimary = () => {\n\n const 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