Product Experimentation and A/B Testing for Decision Makers

Making product changes based on gut feeling often leads to wasted resources and poor user experiences. Learning how to design and run statistically valid experiments ensures your product iterations actually drive growth. This text-based course guides you from the foundational concepts of hypothesis generation to the advanced nuances of modern experimentation. You will learn how to set up reliable tests, interpret results accurately, and avoid the common statistical traps that lead to false positives. What you'll learn: Understand the fundamental statistics of A/B testing, including statistical power, significance, and sample size calculation; Formulate clear, testable hypotheses aligned with key business and product metrics; Design rigorous experiments using modern frameworks to minimize bias and external noise; Analyze test results using both Frequentist and Bayesian statistical approaches to make confident recommendations; Identify and avoid common experimentation pitfalls such as peeking, selection bias, and sample ratio mismatch; Apply modern experimentation concepts like feature flagging, multi-armed bandits, and sequential testing. The course begins with core terminology, metrics definitions, and statistical basics before walking you through the end-to-end lifecycle of a product experiment. You will read real-world scenarios and complete written analysis exercises to solidify your understanding of experiment design. This course is designed for aspiring product managers, growth marketers, and data analysts who are new to experimentation; no prior advanced background in statistics is required. Start reading today to build a culture of reliable, data-driven experimentation in your team.