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…
Updated Sep 17, 2018: Minor spelling and language corrections, updates related to role of randomization and external validity / generalizability. Google Optimize is the latest attempt from Google to deliver an A/B testing product. Previously we had “Google Website Optimizer”, then we had “Content Experiments” within Google Analytics, and now we have the latest iteration: […] 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…
What is the goal of A/B testing? How long should I run a test for? Is it better to run many quick tests, or one long one? How do I know when is a good time to stop testing? How do I choose the significance threshold for a test? Is there something special about 95%? […] Read more…
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…
The question of whether one should run A/B tests (a.k.a online controlled experiments) using one-tailed versus two-tailed tests of significance was something I didn’t even consider important, as I thought the answer (one-tailed) was so self-evident that no discussion was necessary. However, while preparing for my course on “Statistics in A/B Testing” for the ConversionXL […] Read more…
The concept of statistical significance is central to planning, executing and evaluating A/B (and multivariate) tests, but at the same time it is the most misunderstood and misused statistical tool in internet marketing, conversion optimization, landing page optimization, and user testing. This article attempts to lay it out in as plain English as possible: covering […] Read more…
Let’s get this out of the way from the very beginning: most “A/B tests” are in fact multivariate (MVT) tests, a.k.a. A/B/n tests. That is, most of the time when you read about “A/B testing” the term also encompasses multivariate testing. The only reason to specifically differentiate between A/B and MVT is when someone wants to […] 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…
Now, this is not a new topic on this blog. I’ve discussed the issue of optional stopping based on “achieving” or “reaching” statistical significance in my Why Every Internet Marketer Should be a Statistician post more than two years ago and others have touched on it as well – both before and after me. However, the issue […] Read more…
Fantasy vs the Real World: Naive Bayesian AB Testing vs Proper Statistical Inference This post is addressed at a certain camp of proponents and practitioners of A/B testing based on Bayesian statistical methods, who claim that outcome-based optional stopping, often called data peeking or data-driven stopping, has no effect on the statistics and thus inferences […] Read more…
This overview is a little different than most overeviews out there. It will focus on three specific basic statistical concepts and would show if and how well they are explored in 18 of the top books on Conversion Optimization, A/B testing, web analytics, SEO & PPC/AdWords. The three concepts this review focuses on are statistical […] 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.