Articles on average revenue per user

Stop AbUsing the Mann-Whitney U Test (MWU)

Mann-Whitney-U Test

The Mann Whitney U Test (MWU), also known as the Wilcoxon Rank Sum Test and the Mann-Whitney-Wilcoxon Test, continues to be advertised as the go-to test for analyzing non-normally distributed data. In online experimentation it is often touted as the most suitable for analyses of non-binomial metrics with typically non-normal (skewed) distributions such as average […] 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 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 more […] 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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