It’s been almost ten years since I published the white paper “Efficient A/B Testing in Conversion Rate Optimization: The AGILE Statistical Method” (2017). It argues that Group-Sequential Tests (GSTs) are ideally-suited for online A/B testing. I figure it is a good time to reflect back on my search for the best statistical model and see […] 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…
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…
This short post presents several changes, updates, and new tool functionalities released in the past couple of months – some very recent, some less so. The updates concern all statistical calculators for design and analysis of A/B tests at Analytics-Toolkit.com and consist of the following: New tool: Statistical Calculator for Meta Analysis Quality of life […] Read more…
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…
The problem most-often faced by owners of websites who want to take a scientific approach to improving them by using A/B testing is that they might have relatively small revenue. Thus, when the ROI calculation for the A/B test is done it might turn out that it is economically unfeasible to test. In some cases, […] Read more…
I got a question today about our AGILE A/B testing calculator and the statistics behind it and realized that I’m yet to write a dedicated post explaining the efficiency gains from using the method in more detail. This despite the fact that these speed gains are clearly communicated and verified through simulation results presented in our AGILE […] Read more…
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…
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…
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…
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…
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.