Tag: abtesting

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.

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.

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,…