Published inBellStatistically speaking: How to (properly) report A/B testing resultsThis blog highlights common reporting errors and offers practical guidance for presenting tests results effectively.Dec 24, 20241Dec 24, 20241
Published inBellWhen allocation point and exposure point differA common issue in A/B testing is when the allocation point does not align with the exposure point. Why it matters and how to address it.Nov 25, 2024Nov 25, 2024
Published inBellOne tailed vs. two tailed testsChoosing between one and two-tailed hypothesis affects every stage of A/B testing. Learn why, and explore the pros & cons of each approach.Nov 6, 2024Nov 6, 2024
Published inBellIt’s normal not to be normal(ly distributed) — what to do when data is not normally distributedWhy deviations from normality are often not a significant concern in A/B testing, and what are the alternative methods to t-testOct 21, 2024Oct 21, 2024
Published inTowards Data ScienceWhy the uplift in A/B tests often differs from real-world resultsExplore why the uplift seen in A/B tests often differs from real-world outcomes, and get insights on how to manage expectations…Sep 8, 2024Sep 8, 2024
Published inBellSize matters: How to plan test duration when using CUPEDLearn how to use CUPED, a powerful technique that enhances the sensitivity of your tests and helps in resource optimization.Aug 18, 20241Aug 18, 20241
Published inTowards Data ScienceBayesian A/B Testing Falls ShortThere’s a disconnect between the industry’s enthusiasm for Bayesian testing and its actual contribution, validity, and effectiveness.Jun 26, 20243Jun 26, 20243
Published inTowards Data ScienceFour Ways to Improve Statistical Power in A/B Testing (Without Increasing Test Duration, Duh)Learn how Allocation, Effect Size, CUPED & Binarization can help you improve statistical power without prolonging test durationsMay 22, 2024May 22, 2024