Author Archives: Georgi Georgiev

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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Q&A on Sequential Statistics in A/B Testing

Sequential testing QA

Sequential statistics are gathering interest and there are more and more questions posed by CROs looking into the matter. For this article I teamed up with Lucia van den Brink, a distinguished CRO consultant who recently started using Analytics Toolkit and integrated frequentist sequential testing into her client workflow. In this short interview she asks […] Read more…

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Sequential Testing is About Improving Business Returns

Sequential Testing Efficiency

A central feature of sequential testing is the idea of stopping “early”, as in “earlier compared to an equivalent fixed-sample size test”. This allows running A/B tests with fewer users and in a shorter amount of time while adhering to the targeted error guarantees. For example, a test may be planned with a maximum duration […] Read more…

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False Positive Risk in A/B Testing

False positive risk in A/B testing

Have you heard how there is a much greater probability than generally expected that a statistically significant test outcome is in fact a false positive? In industry jargon: that a variant has been identified as a “winner” when it is not. In demonstrating the above the terms “False Positive Risk” (FPR), “False Findings Rate” (FFR), […] Read more…

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A lightweight Google Analytics 4 integration

Lightweight Google Analytics 4

Google Analytics 4 has been a let down in many aspects based on every discussion I’ve seen and had with professionals of all stripes – marketers, advertising specialists, CROs, GA professionals, online experimentation experts, etc. One of the less discussed issues it brought with it is the default heavyweight GTAG library integration it comes with. […] Read more…

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Analytics Toolkit to discontinue Google Analytics-related functionalities

Discontinuing Google Analytics Functionalities

Analytics Toolkit was conceived in 2012 as a set of tools that automate essential Google Analytics-related tasks and augment the GA functionalities in various ways. This goal was achieved in the years since with the release of over a dozen tools utilizing the Google Analytics API. These were accompanied by dozens of in-depth technical articles […] Read more…

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How to Run Shorter A/B Tests?

Shorter A/B Tests

Running shorter tests is key to improving the efficiency of experimentation as it translates to smaller direct losses from testing inferior experiences and also less unrealized revenue due to late implementation of superior ones. Despite this, many practitioners are yet to start conducting tests at the frontier of efficiency. This article presents ways to shorten […] Read more…

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Comparison of the statistical power of sequential tests: SPRT, AGILE, and Always Valid Inference

Power and Average Sample Size of Sequential Tests

In A/B testing sequential tests are gradually becoming the norm due to the increased efficiency and flexibility that they grant practitioners. In most practical scenarios sequential tests offer a balance of risks and rewards superior to that of an equivalent fixed sample test. Sequential monitoring achieves this superiority by trading statistical power for the ability […] Read more…

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Statistical Power, MDE, and Designing Statistical Tests

Statistical Power and MDE Demystified

One topic has surfaced in my ten years of developing statistical tools, consulting, and participating in discussions and conversations with CRO & A/B testing practitioners as causing the most confusion and that is statistical power and the related concept of minimum detectable effect (MDE). Some myths were previously dispelled in “Underpowered A/B Tests – Confusions, […] Read more…

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What Can Be Learned From 1,001 A/B Tests?

Meta Analysis

How long does a typical A/B test run for? What percentage of A/B tests result in a ‘winner’? What is the average lift achieved in online controlled experiments? How good are top conversion rate optimization specialists at coming up with impactful interventions for websites and mobile apps? This meta-analysis of 1,001 A/B tests analyzed using […] Read more…

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Fully Sequential vs Group Sequential Tests

Sequential Testing Compared

What is the best design for a statistical test with sequential evaluation of the data at multiple points in time? This is a question anyone who has realized that unaccounted for peeking with intent to stop is the bane of A/B testing eventually comes to ask. So how does one go about answering that? This […] Read more…

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When Session-Based Metrics Lie

Per Session Metrics In AB Testing

In online A/B testing it is not uncommon to see session-based metrics being used as the primary performance indicator. Session-based conversion rates and session-based averages (like average revenue per session, in likeness to ARPU) are often reported by default in software by prominent vendors, including Google Optimize and Google Analytics. This widespread availability makes session-based […] Read more…

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