Published inData Science CollectiveYou can have it all: Parallel Testing in A/B TestingExplore Parallel Testing key advantages and potential challenges, and share best practices for successful implementationMay 13May 13
Published inData Science CollectiveWhen being ‘good enough’ is enough: Understanding non-inferiority testsExplore superiority tests and non-inferiority tests, and get practical guide to designing and interpreting non-inferiority testsMay 13May 13
Published inData Science CollectiveWhat to do when you encounter Sample Ratio Mismatch in A/B TestingLearn why checking for SRM is crucial, explore common reasons it occurs, and learn how to detect, diagnose, and address it.May 10May 10
Published inData Science CollectiveWhen one bad apple spoils the barrel: Tackling outliers in A/B TestingLearn why outliers can be problematic, explore the challenges of identifying and handling them, and get practical guidelines.May 10A response icon5May 10A response icon5
Published inData Science CollectiveStatistically 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, 2024A response icon1Dec 24, 2024A response icon1
Published inData Science CollectiveWhen 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 inData Science CollectiveOne 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, 2024A response icon1Nov 6, 2024A response icon1
Published inData Science CollectiveIt’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 inData Science CollectiveWhy 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 inData Science CollectiveSize 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, 2024A response icon1Aug 18, 2024A response icon1
