Practical A/B Testing: From Hypothesis to Statistical Analysis

Making website changes based on guesswork can hurt your conversion rates and user experience. To grow your business effectively, you need to rely on clear, statistically sound data through structured A/B testing. In this text-based course, you will transition from guessing to making confident, data-driven decisions. You will learn the entire lifecycle of an A/B test, starting with foundational statistical concepts and moving all the way to analyzing real-world experiment results. What you'll learn: - Formulate clear, testable hypotheses based on user behavior data and business goals. - Calculate the required sample size and test duration to ensure statistically significant results. - Understand fundamental statistical concepts including p-values, confidence intervals, and statistical power. - Detect and avoid common experimentation pitfalls like Sample Ratio Mismatch (SRM) and early peeking. - Analyze test data using standard statistical methods to make confident, data-driven product decisions. The course begins with the foundational vocabulary of experimentation and hypothesis design. You will then progress through step-by-step guides on sample calculation, test execution, and statistical analysis using clear explanations and practical written scenarios. Designed for beginner product managers, marketers, and data analysts, this course requires no prior background in advanced statistics. Start reading today to master the science of data-driven website optimization.