Implementing an A/B test correctly means locking the hypothesis, primary metric, sample size, and stop rule before you build; assigning traffic randomly and stickily; verifying tracking before launch; QA-ing both variants across the full funnel; and refusing to call the result until the pre-calculated sample size and a full business cycle are complete. Most failed … Continue reading How to Implement and Execute an A/B Test Correctly