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...
Are you falling victim to the peeking problem? Learn how to design and run an experiment properly.| www.evanmiller.org
Multivariate testing modifies multiple variables simultaneously to determine the best combination of variations on those elements of a website or mobile app.| Optimizely
A/B testing (i.e. running a controlled experiment, normally in a digital environment like a website or an application) is prevalent nowadays. All of the biggest tech companies you know – Microsoft, Netflix, Booking.com, Google – run thousands of experiments per year. Now it’s weird if you don’t run A/B tests. There are dozens of tools ... Read more| Alex Birkett