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 is required in order to stop early when results are unpromising or negative and how it works in practice. We’re reviewing this from the standpoint of the AGILE me...| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
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.| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
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 paper and a statistical calculator for A/B testing practitioners. In this post we’ll cover briefly the need for a new method, some highligh...| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
This is a comprehensive guide to the different types of costs and benefits, risks and rewards related to A/B testing. Understanding them in detail should be valuable to A/B testers and businesses considering whether to engage in A/B testing or not, what to A/B test and what not to test, etc. As far as I am aware, this is the first attempt to systematically review all the different factors contributing to the return on investment from the process of A/B testing. Here I will cover A/B testing m...| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
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.| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
The one-stop-shop for statistical planning and analysis of online A/B tests. Analytics Toolkit's advanced A/B test statistical calculator enable your A/B testing program to reach new levels of statistical rigor and efficiency. Plan and analyze A/B tests with ease and get results you can trust.| www.analytics-toolkit.com
