Underpowered A/B Tests – Confusions, Myths, and Reality

Underpowered A/B Tests

In recent years a lot more CRO & A/B testing practitioners have started paying more attention to the statistical power of their online experiments, at least based on my observations. While this a positive development for which I hope I had contributed somewhat, it comes with the inevitable confusions and misunderstandings surrounding a complex concept […] 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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The Cost of Not A/B Testing – a Case Study

The cost of not a/b testing

Most of the time when discussing A/B testing, regardless of context, we discuss costs such as the expense of running an experimentation program, of shipping ‘winners’ to production. Only rarely do I see references to the less obvious, but usually more important costs in terms of opportunity cost (incurred during testing) and the cost of […] Read More…

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What Can You Learn From Running an A/B Test for 2 Years?

AB Test Case Study

We just concluded an A/B test on Analytics-Toolkit.com that has been left to run for just over 2 years. And it failed, as in failing to demonstrate a statistically significant effect based on the significance threshold it was designed for. Has it been a waste of time, though, or can we actually learn something from […] Read More…

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Does Your A/B Test Pass the Sample Ratio Mismatch Check?

Sample Ratio Mismatch AB Testing

Most, if not all successful online businesses nowadays rely on one or more systems for conducting A/B tests in order to inform business decisions ranging from simple website or advertising campaign interventions to complex product and business model changes. While testing might have become a prerequisite for releasing the tiniest of changes, one type of […] Read More…

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AGILE A/B Testing Update: Custom API & Support for Non-Binomial Data

AGILE A/B Testing Tool Updates

I’m happy to announce the release of two long-awaited features for our AGILE A/B Testing Calculator: Support for non-binomial metrics like average revenue per user A new custom API for sending experiment data to the calculator Below is an explanation of each of these new features in some detail. Support for Non-Binomial Data While our […] Read More…

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