Articles from 2017

Risk vs. Reward in A/B Tests: A/B testing as Risk Management

Risks vs Rewards in AB Testing

What is the goal of A/B testing? How long should I run a test for? Is it better to run many quick tests, or one long one? How do I know when is a good time to stop testing? How do I choose the significance threshold for a test? Is there something special about 95%? […] Read more…

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Costs and Benefits of A/B Testing: A Comprehensive Guide

Costs and Benefits in AB Testing

This is a comprehensive guide to the different types of costs and benefits, risks and rewards related to A/B testing. Understanding them in detail should be valuable to A/B testers and businesses considering whether to engage in A/B testing or not, what to A/B test and what not to test, etc. As far as I […] Read more…

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Statistical Significance for Non-Binomial Metrics – Revenue per User, AOV, etc.

Non-Binomial Significance - Revenue, Per User Metrics

In this article I cover the method required to calculate statistical significance for non-binomial metrics such as average revenue per user, average order value, average sessions per user, average session duration, average pages per session, and others. The focus is on A/B testing in the context of conversion rate optimization, landing page optimization and e-mail […] Read more…

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One-tailed vs Two-tailed Tests of Significance in A/B Testing

Two-tailed vs one-tailed test

The question of whether one should run A/B tests (a.k.a online controlled experiments) using one-tailed versus two-tailed tests of significance was something I didn’t even consider important, as I thought the answer (one-tailed) was so self-evident that no discussion was necessary. However, while preparing for my course on “Statistics in A/B Testing” for the ConversionXL […] Read more…

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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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Running Multiple A/B Tests at The Same Time: Do’s and Don’ts

Concurrent AB Tests

Can running multiple A/B tests at the same time lead to interferences that result in choosing inferior variants? Does running each A/B test in a silo improve or worsen the situation? If there is any danger, how great is it and how much should we be concerned about it? In this post, I’ll try to answer […] Read more…

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Top 10 ways to ruin your Google Analytics data and how to avoid them

No Pulse - No Data

A lot of businesses rely on Google Analytics to assess the performance of their online efforts with regards to online sales, marketing, support, or just providing users with information about a brand or a product. Any measurements and conclusions based on them, however, are only as good as the accuracy and reliability of the data […] Read more…

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