Posted on Categories data science, Statistics, TutorialsTags , , , , , , 2 Comments on A clear picture of power and significance in A/B tests

A clear picture of power and significance in A/B tests

A/B tests are one of the simplest reliable experimental designs.

Controlled experiments embody the best scientific design for establishing a causal relationship between changes and their influence on user-observable behavior.

“Practical guide to controlled experiments on the web: listen to your customers not to the HIPPO” Ron Kohavi, Randal M Henne, and Dan Sommerfield, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 2007 pp. 959-967.

The ideas is to test a variation (called “treatment” or “B”) in parallel with continuing to test a baseline (called “control” or “A”) to see if the variation drives a desired effect (increase in revenue, cure of disease, and so on). By running both tests at the same time it is hoped that any confounding or omitted factors are nearly evenly distributed between the two groups and therefore not spoiling results. This is a much safer system of testing than retrospective studies (where we look for features from data already collected).

Interestingly enough the multi-armed bandit alternative to A/B testing (a procedure that introduces online control) is one of the simplest non-trivial Markov decision processes. However, we will limit ourselves to traditional A/B testing for the remainder of this note. Continue reading A clear picture of power and significance in A/B tests