Category Archives: Statistical Significance

The Case for Non-Inferiority A/B Tests

The Case for Non-Inferiority Testing

In this article, I explore the concept of non-inferiority A/B tests and contrast it to the broadly accepted practice of running superiority tests. I explain where non-inferiority tests are necessary and how a CRO/LPO/UX testing specialist can make use of this new approach to A/B testing to run much faster tests, and to ultimately achieve […] Read More…

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Statistical Significance in A/B Testing – a Complete Guide

Statistical Significance P Value

The concept of statistical significance is central to planning, executing and evaluating A/B (and multivariate) tests, but at the same time it is the most misunderstood and misused statistical tool in internet marketing, conversion optimization, landing page optimization, and user testing. This is not my first take on the topic, but it is my best […] Read More…

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Multivariate Testing – Best Practices & Tools for MVT (A/B/n) Tests

Multivariate AB Tests / MVT Testing

Let’s get this out of the way from the very beginning: most “A/B tests” are in fact multivariate (MVT) tests, a.k.a. A/B/n tests. That is, most of the time when you read about “A/B testing” the term also encompasses multivariate testing. The only reason to specifically differentiate between A/B and MVT is when someone wants to […] Read More…

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The Importance of Statistical Power in Online A/B Testing

Statistical Power and Test Sensitivity

What is Statistical Power? In null-hypothesis statistical testing (NHST) – the procedure most commonly applied in A/B tests, there are two types of errors that practitioners should care about, type I and type II errors. Type I is the probability of the test procedure to falsely reject a true null hypothesis. Type II error is […] Read More…

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5 Reasons to Go Bayesian in AB Testing – Debunked

Frequentist vs Bayesian A/B Testing

As someone who spent a good deal of time on trying to figure out how to run A/B tests properly and efficiently, I was intrigued to find a slide from a presentation by VWO®’s data scientist Chris Stucchio, where he goes over the main reasons that caused him and the VWO® team to consider and […] Read More…

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The Bane of AB Testing: Reaching Statistical Significance

Illusory Results AB Testing

Now, this is not a new topic on this blog. I’ve discussed the issue of optional stopping based on “achieving” or “reaching” statistical significance in my Why Every Internet Marketer Should be a Statistician post more than two years ago and others have touched on it as well – both before and after me. However, the issue […] Read More…

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