How Bayesian A-B Testing Avoids False Positives

Episode 12 of The Data Science Podcast dives into why traditional frequentist A/B testing can lead to false positives and how a Bayesian approach fixes it. Lucas and Luna walk through a concrete example: an e-commerce team testing a new checkout flow that looked like a winner at 5,000 visitors but collapsed at 10,000. They explain p-hacking, the peek problem, and how Bayesian methods with prior distributions, posterior probabilities, and expected loss give you more reliable decisions with smaller sample sizes. No math PhD required — just practical intuition for data scientists and product managers who run tests every week. This episode also covers the free, ad-free mission of the podcast and how listeners can support it. #BayesianA-BTesting #FrequentistStatistics #p-value #p-hacking #DataScience #A-BTesting #BayesianInference #PriorDistribution #PosteriorProbability #ExpectedLoss #FalsePositive #EcommerceTesting #ProductManagement #Experimentation #MachineLearning #StatisticalMethods #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo