Why Your A-B Test Loses Statistical Significance

What this episode covers

Lucas and Luna explain 'peeking,' the practice of checking A-B test results early, which inflates false positive rates. They break down why stopping a test the moment it hits 95% confidence is a statistical trap, using examples from Optimizely and booking sites. The episode explores the concept of sequential testing and the 'always valid' p-value method, then discusses how persistent peeking led one travel company to implement stricter governance. They also share a simple rule: decide the sample size and duration before the test starts. A short donation segment highlights listener support via buymeacoffee.com/fexingo. #Peeking #A-BTesting #StatisticalSignificance #FalsePositive #Optimizely #SequentialTesting #PValue #ConversionRateOptimization #CRO #Marketing #DataScience #Experimentation #BusinessPodcast #MarketingPodcast #FexingoBusiness #LucasAndLuna #SampleSize #ExperimentDesign Keep every episode free: buymeacoffee.com/fexingo

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Frequently Asked Questions

How long is this episode of Conversion Rate Optimization with Fexingo: A/B Testing, Landing Pages, and CRO Strategy?

This episode is 6 minutes long.

When was this Conversion Rate Optimization with Fexingo: A/B Testing, Landing Pages, and CRO Strategy episode published?

This episode was published on June 9, 2026.

What is this episode about?

Lucas and Luna explain 'peeking,' the practice of checking A-B test results early, which inflates false positive rates. They break down why stopping a test the moment it hits 95% confidence is a statistical trap, using examples from Optimizely and...

Can I download this Conversion Rate Optimization with Fexingo: A/B Testing, Landing Pages, and CRO Strategy episode?

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