Run split tests faster, more efficiently and with better accuracy! The A/B testing significance calculator provides an advanced statistical approach to A/B and Multivariate testing in Conversion Rate Optimization, landing page optimization, e-mail template optimization, mobile app optimization and more. With AGILE A/B testing you get control over statistical significance and power while doing interim analysis and requiring less users to complete tests, on average. This A/B test calculator als...| www.analytics-toolkit.com
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 statistical method white paper [1].| 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...
Learn the meaning of Risk-Reward Analysis in the context of A/B testing, a.k.a. online controlled experiments and conversion rate optimization. Detailed definition of Risk-Reward Analysis, related reading, examples. Glossary of split testing terms.| www.analytics-toolkit.com
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
One topic has surfaced in my ten years of developing statistical tools, consulting, and participating in discussions and conversations with CRO & A/B testing practitioners as causing the most confusion and that is statistical power and the related concept of minimum detectable effect (MDE). Some myths were previously dispelled in “Underpowered A/B tests – confusions, myths, and reality”, “A comprehensive guide to observed power (post hoc power)”, and other works. Yet others remain.| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
How long does a typical A/B test run for? What percentage of A/B tests result in a ‘winner’? What is the average lift achieved in online controlled experiments? How good are top conversion rate optimization specialists at coming up with impactful interventions for websites and mobile apps?| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...