Why Peeking at A-B Tests Early Wastes Your Data

Episode 43 of Conversion Rate Optimization with Fexingo dives into one of the most common and costly mistakes in A-B testing: peeking at results before the experiment ends. Lucas explains the statistical problem of 'peeking bias' — how repeatedly checking significance inflates false positives — and illustrates it with a concrete example from a real e-commerce checkout flow. Luna pushes back on the temptation to stop early when a variant looks like a winner, and they walk through a practical framework: pre-register your test duration, set a minimum sample size, and resist the urge to peek. The episode also covers how to handle ethical dilemmas when a test seems to harm user experience mid-run. By the end, listeners will understand why 'just peeking' corrupts the very data they're relying on — and how to build discipline around it. #ABTesting #PeekingBias #StatisticalSignificance #DataIntegrity #CROStrategy #ConversionRateOptimization #Experimentation #MarketingAnalytics #EcommerceTesting #TestDesign #FrequentistStatistics #FalsePositive #SampleSize #HypothesisTesting #CheckoutOptimization #FexingoBusiness #BusinessPodcast #Marketing Keep every episode free: buymeacoffee.com/fexingo