Why Your A-B Test Sample Size Is Probably Wrong

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Why Your A-B Test Sample Size Is Probably Wrong

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Lucas and Luna dive into one of the most overlooked errors in A-B testing: miscalculating sample size. Using a real-world case from a mid-market SaaS company, they show how a seemingly valid test with 10,000 visitors per variant actually needed 50,000 to detect a realistic 2 percent lift. They explain the math behind statistical power, why most online calculators give misleading defaults, and how to fix your sample size planning tomorrow. Episode 50 of Conversion Rate Optimization with Fexingo. #ABTesting #SampleSize #StatisticalPower #ConversionRateOptimization #CRO #Marketing #MarketingStrategy #DataScience #Statistics #SaaS #Experimentation #Business #BusinessPodcast #FexingoBusiness #Podcast #LucasAndLuna #ExperimentationCulture #TestDesign Keep every episode free: buymeacoffee.com/fexingo

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