A/B Testing & Statistical Experimentation

О курсе

By the end of this course, you will be able to design, execute, and interpret controlled experiments; quantify and visualize statistical uncertainty; apply resampling and simulation techniques; and perform post-hoc analyses to compare multiple experimental variants with confidence. This course equips you with the practical statistical foundations needed to make reliable, data-driven decisions through experimentation. You’ll learn how to move from formulating clear hypotheses to interpreting results that separate real effects from random noise. Through a progression of modules, you will build fluency in confidence intervals, sample size justification, resampling methods such as bootstrapping, Monte Carlo simulation, and advanced hypothesis testing techniques including ANOVA and chi-squared tests. What makes this course unique is its emphasis on how statistical tools support real experimental decisions, not just how they are calculated. Drawing on expertise from multiple academic and industry perspectives, the course shows you the same experimentation concepts through complementary lenses—conceptual, graphical, and applied—so you develop deeper intuition and judgment. You’ll gain the ability to reason about uncertainty, avoid common pitfalls in experimentation, and communicate results clearly and responsibly. Whether you are new to experimentation or looking to strengthen your statistical rigor, this course will help you design better experiments, interpret results with confidence, and turn data into trustworthy insights.

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