Several charges are commonly thrown at A/B testing while considering it or even after it has become standard practice in a company. They may come from product teams, designers, developers, or management, and can be summed up like this: A good way to address these and to make the business case for experimentation is to […] Read more…
The above is a question asked by some practitioners of A/B testing, as well as a number of their clients when examining the outcome of an online controlled experiment. It may be raised regardless if the outcome is statistically significant or not. In both cases the fact the observed effect in an A/B test is […] Read more…
Observed power, often referred to as “post hoc power” and “retrospective power” is the statistical power of a test to detect a true effect equal to the observed effect size. “Detect” in the context of a statistical hypothesis test means to result in a statistically significant outcome. Some calculators aimed at A/B testing practitioners use […] Read more…
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…
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…
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…
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…
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…
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…
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…
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…
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…
Take your A/B testing program to the next level
Want to take your A/B tests to the next level?
If you enjoyed this article and want to read more great content like it make sure to check out the book “Statistical Methods in Online A/B Testing” by the author, Georgi Georgiev, and take your experimentation program to the next level.