Bayesian A/B Testing: Evaluating Conversions and User Revenue

Making data-driven decisions requires more than just running split tests; you need to analyze the results with statistical rigor to avoid costly mistakes. This text-based course introduces you to the fundamentals of A/B testing, highlighting how Bayesian modeling can provide clearer, more intuitive insights than traditional methods. You will transition from understanding basic split-testing concepts to implementing advanced Bayesian analysis for key product metrics. You will learn how to evaluate conversion rates, compare average values, handle multiple comparisons, and assess transactional revenue per user using clean, modern statistical approaches. What you'll learn: - Understand the foundational concepts of hypothesis testing, statistical power, and sample sizes. - Apply Bayesian modeling techniques to compare conversion rates and mean values across test groups. - Manage the multiple comparisons problem to prevent false positives in multi-variant tests. - Analyze skewed metric distributions like transactional revenue per user. - Practice interpreting posterior distributions and credible intervals to make confident business decisions. The course starts with essential testing terminology and basic probability before guiding you step-by-step through Bayesian formulas, practical code-based examples, and revenue-tracking scenarios. It is designed for aspiring data analysts, product managers, and marketers who want to understand the mechanics of modern A/B testing without needing an advanced math degree. Start reading today to elevate your product analytics skills with modern Bayesian methods.