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.

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