An Introduction to Bayesian A/B Testing

A/B testing, also known as split testing, helps businesses optimize conversion rates by experimenting with different webpage versions. The post compares frequentist and Bayesian methods for analyzing A/B test results. It highlights the limitations of the Chi2 test in frequentist settings and demonstrates Bayesian modeling using Python's PyMC package. A more complex example of modeling customer behavior post-intervention showcases Bayesian flexibility in uncertain data scenarios. Bayesian inference is advocated for its intuitive interpretation and adaptability, especially when data is sparse and uncertainty modeling is crucial.