Articles exploring a-b-testing-guide-series through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
12 articles
Compare Bayesian and Frequentist approaches to A/B testing. Understand the practical differences, when each excels, and why the debate matters less than your fundamentals.
Step-by-step guide to setting up A/B tests properly — from writing testable hypotheses to choosing between server-side and client-side tools to the QA checklist before launch.
Understand why A/B test results might not hold in the real world. Learn about seasonality, selection bias, novelty effects, and how to protect your conclusions from external validity threats.
Learn when you can safely run multiple A/B tests simultaneously and when interaction effects will corrupt your results. Includes traffic allocation strategies and detection methods.
Stop losing experiment learnings. Build an A/B test archive and knowledge base that compounds institutional knowledge, prevents duplicate tests, and accelerates onboarding.
Learn how to calculate the right sample size and test duration for A/B tests. Understand regression to the mean, why peeking kills tests, and the magic number myth.
Learn how to prioritize your A/B test backlog using data-driven frameworks like PXL. Stop testing based on opinions and start testing based on evidence and potential impact.
Master the four-phase A/B testing process that separates systematic optimization from random testing. Learn why most teams skip the first two phases and why that kills their programs.
Go beyond the textbook definition of A/B testing. Learn what controlled experimentation really means for digital products, why most teams get it wrong, and the five components every valid test requires.
Learn how to properly analyze A/B test results beyond the dashboard green light. Master segmentation, effect size interpretation, and honest reporting that builds credibility.
Discover the six research methods that separate high-impact A/B tests from random guessing. From heuristic analysis to user testing, learn how to find the problems worth solving.
Demystify A/B testing statistics — p-values, confidence intervals, Type I and Type II errors, and one-tail vs two-tail tests explained in plain English with business context.