Articles on confidence intervals

A/B Testing Statistics – A Concise Guide for Non-Statisticians

AB Testing Statistics

Navigating the maze of A/B testing statistics can be challenging. This is especially true for those new to statistics and probability. One reason is the obscure terminology popping up in every other sentence. Another is that the writings can be vague, conflicting, incomplete, or simply wrong, depending on the source. Articles sprinkled with advanced math, […] Read more…

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P-values and Confidence Intervals Explained

P-values and confidence intervals explained

Hundreds if not thousands of books have been written about both p-values and confidence intervals (CIs) – two of the most widely used statistics in online controlled experiments. Yet, these concepts remain elusive to many otherwise well-trained researchers, including A/B testing practitioners. Misconceptions and misinterpretations abound despite great efforts from statistics educators and experimentation evangelists. […] Read more…

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The Perils of Poor Data Visualization in CRO & A/B Testing

As any UX & CRO expert should now, the way we present information matters a lot both in terms of how well it is understood and in terms of the probability that it will lead to the desired action. A/B testing calculators and other tools of the trade are no exception and here I will […] Read more…

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Designing successful A/B tests in Email Marketing

The process of A/B testing (a.k.a. online controlled experiments) is well-established in conversion rate optimization for all kinds of online properties and is widely used by e-commerce websites. On this blog I have already written in depth about the statistics involved as well as the ROI calculations in terms of balancing risk and reward for […] Read more…

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Confidence Intervals & P-values for Percent Change / Relative Difference

In many controlled experiments, including online controlled experiments (a.k.a. A/B tests) the result of interest and hence the inference made is about the relative difference between the control and treatment group. In A/B testing as part of conversion rate optimization and in marketing experiments in general we use the term “percent lift” (“percentage lift”) while in […] 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 article attempts to lay it out in as plain English as possible: covering […] Read more…

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Bayesian AB Testing is Not Immune to Optional Stopping Issues

Fantasy vs real world in bayesian ab testing

Fantasy vs the Real World: Naive Bayesian AB Testing vs Proper Statistical Inference This post is addressed at a certain camp of proponents and practitioners of A/B testing based on Bayesian statistical methods, who claim that outcome-based optional stopping, often called data peeking or data-driven stopping, has no effect on the statistics and thus inferences […] Read more…

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