A/B Testing: Case Studies for Product Analytics

A/B testing is the cornerstone of data-driven product development, yet designing and interpreting tests with statistical rigor remains a significant challenge. This course provides a hands-on, case-study approach to mastering experimental design, statistical analysis, and decision-making techniques used by professional product analysts. You will gain confidence in moving beyond simple comparisons to manage complex product experiments. What you'll learn: * Learn the core terminology and statistical principles behind hypothesis testing and A/B test design. * Design robust experiments, calculate required sample sizes, and ensure adequate statistical power. * Apply practical analysis techniques to realistic product case studies, focusing on primary and guardrail metrics. * Understand the mechanics of sequential testing and modern multi-armed bandit strategies. * Practice interpreting complex test results and formulating actionable, data-driven product recommendations. * Explore foundational Bayesian statistical concepts and their application in risk management and resource allocation. The course begins by establishing foundational definitions and statistical concepts before immediately moving into practical, structured case studies that simulate real product challenges. You will work through written exercises designed to reinforce theory and build analytical muscle. This course is designed for beginners interested in data science, marketing analytics, or product management. No prior experience with statistical programming or A/B testing is required. Start building your expertise in data-driven decision-making today.