Lucas and Luna dive into Bayesian A/B testing, a method that's quietly replacing traditional frequentist approaches in data science. They break down how it works, why it's more intuitive, and where it falls short. The episode centers on a real case: how a major retailer used Bayesian testing to optimize their checkout flow, cutting decision time from weeks to days. Lucas explains the math behind prior probabilities and posterior distributions without the jargon, while Luna questions whether Bayesian methods can really scale in big-tech environments. They also touch on the common pitfalls, like choosing a bad prior or misinterpreting results. By the end, listeners will understand the key difference between 'is this statistically significant?' and 'what's the probability this variant is better?'—and why the latter question is often more useful in practice.

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