Tag: statistics

Pvalue is the probability that the treatment effect is larger than zero (under certain conditions)
a.k.a. why you should (not ?) use uninformative priors in Bayesian A/B testing.

Estimating longterm detection, win, and error rates in A/B testing
How to estimate the probability of detecting (a positive) treatment over a series of experiments? I use an (admittedly weird) fusion of frequentist concepts and Bayesian tooling to get to an answer.

Estimating home court advantage in Lithuanian Basketball League with Gaussian Processes
I was looking for an excuse to play around with Gaussian Processes in a Bayesian Inference setting, and decided to revisit an older project about basketball in Lithuania. Just in time for this year’s finals!

Modeling tenure effects the Bayesian way
After learning new things in Statistical Rethinking class, I took on to play around with an ageperiodcohortlike model for disentangling tenure effects from seasonality & other factors. The Bayesian way.

Getting faster to decisions in A/B tests – part 2: misinterpretations and practical challenges of classical hypothesis testing
Null hypothesis test of means is the most basic statistical procedure used in A/B testing. But the concepts built into it are not exactly intuitive. I go through 5 practical issues that anyone working with experimentation in business should be aware of.

Getting to decisions faster in A/B tests – part 1: literature review
I set out on a journey to learn what statistical approaches the industry uses to get to faster decisions in A/B testing. This is the first post in the series in which I set the scene and summarize outcomes of my “literature review”.

A/B testing, zeroinflated (truncated) distributions and power
Naive A/B testing just uses ttests or proportion tests, with the assumption that at large sample sizes, the right statistical test does not matter that much. I explore the case of a zeroinflated upperbounded Poisson distribution and find that using the wrong test can require 3x the sample size to achieve the same statistical power,…

Gaussian Processes: a versatile data science method that packs infinite dimensions
Last semester, I learned about Gaussian Processes. They seemed really intriguing at the first glance, and it turned out they are even more intriguing as you dig deeper. This post is an applicationoriented intro to Gaussian Processes. I’ll cover GP regressions, forecasting for time series and usage of GPs in bayesian optimization among other things.

Interpretation of log transformations in linear models: just how accurate is it?
Logtransformations and their interpretation as percentage impact is taught in every introductory regression class. But are most people aware that there is a hidden approximation behind the percentagebased intuition? One that may not be appropriate in some cases?