The first major overhaul of Analytics Toolkit since its release in early 2014 has finally arrived and it brings with it solutions to many of the hard questions facing practitioners when planning and analyzing A/B tests. Conducting statistically rigorous tests while achieving the best return on investment go hand in hand in the new Toolkit. […] Read more…
Many adherents of Bayesian methods put forth claims of superiority of Bayesian statistics and inference over the established frequentist approach based mainly on the supposedly intuitive nature of the Bayesian approach. Rational thinking or even human reasoning in general is Bayesian by nature according to some of them. Others argue that proper decision-making is inherently […] Read more…
(Updated Sep 2022 & Jan 2023 to include updated information about Google Analytics 4) Many analysts, marketers, product managers, UX and CRO professionals nowadays rely on user counts provided by Google Analytics, Adobe Analytics, or similar tools, in order to perform various statistical analyses. Such analyses may involve the statistical hypothesis tests and estimations part […] Read more…
This article will examine the effects of using the HyperLogLog++ (HLL++) cardinality estimation algorithm in applications where its output serves as input for statistical calculations. A prominent example of such a scenario can be found in online controlled experiments (online A/B tests) where key performance measures are often based on the number of unique users, […] Read more…
Sequential analysis of experimental data from A/B tests has been quite prominent in recent years due to the myriad of Bayesian solutions offered by big industry players. However, this type of sequential analysis is not sequential testing proper as these solutions have generally abandoned the idea of testing and therefore error control, substituting it for […] Read more…
In this article I’m revisiting* the topic of frequentist vs Bayesian inference with specific focus on online A/B testing as usual. The present discussion easily generalizes to any area where we need to measure uncertainty while using data to guide decision-making and/or business risk management. In particular, I will discuss each of the following five […] Read more…
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…
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…
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…
The long wait is finally over! “Statistical Methods in Online A/B Testing” can now be found as a paperback and an e-book on your preferred Amazon store. The book is a comprehensive guide to statistics in online controlled experiments, a.k.a. A/B tests, and tackles the difficult matter of statistical inference in a way accessible to […] Read more…
The question “How to test if my website has a small number of users” comes up frequently when I chat to people about statistics in A/B testing, online and offline alike. There are different opinions on the topic ranging from altering the significance threshold, statistical power or the minimum effect of interest all the way […] Read more…
We are happy to present a brand new addition to our website: a comprehensive A/B testing glossary containing terms and abbreviations used testing as part of conversion rate optimization (CRO). Definitions start from very basic things such as “A/B test“, “mean“, “conversion rate” and “revenue per user“, go through “hypothesis“, “null hypothesis“, “standard deviation“, “p-value” […] 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.