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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Futility Stopping Rules in AGILE A/B Testing

Stopping for Lack of Effect (Futility)

In this article we continue our examination of the AGILE statistical approach to AB testing with a more in-depth look into futility stopping, or stopping early for lack of positive effect (lack of superiority). We’ll cover why such rules are helpful and how they help boost the ROI of A/B testing, why a rigorous statistical rule […] Read More…

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Efficient AB Testing with the AGILE Statistical Method

AGILE AB Testing

Don’t we all want to run tests as quickly as possible, reaching results as conclusive and as certain as possible? Don’t we all want to minimize the number of users we send to an inferior variant and to implement a variant with positive lift as quickly as possible? Don’t we all want to get rid of […] Read More…

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Improving ROI in A/B Testing: the AGILE AB Testing Approach

After many months of statistical research and development we are happy to announce two major releases that we believe have the potential to reshape statistical practice in the area of A/B testing by substantially increasing the accuracy, efficiency and ultimately return on investment of all kinds of A/B testing efforts in online marketing: a free white […] 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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