Episode

How Data Scientists Use Bayesian A-B Testing

Podcast
The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations
Published
Jun 8, 2026
Duration seconds
497
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not_requested
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https://audio.fexingo.com/business/the-data-science-podcast/episode-0038.mp3
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https://audio.fexingo.com/business/the-data-science-podcast/episode-0038.mp3
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Summary

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. #DataScience #Technology #BayesianStatistics #ABTesting #MachineLearning #StatisticalModeling #DataDrivenDecisionMaking #PriorProbability #PosteriorDistribution #ConversionRateOptimization #FrequentistVsBayesian #DataSciencePodcast #FexingoBusiness #BusinessPodcast #Experimentation #DecisionScience #EcommerceAnalytics #DataCulture Keep every episode free: buymeacoffee.com/fexingo