Statistics and A/B Testing Fundamentals for Data Analysis

Understanding statistics and experiment design is essential for making sound product and business decisions. This course breaks down fundamental statistical theory and practical A/B testing methods into clear, structured written lessons. You will build a solid theoretical foundation before applying key concepts to real-world scenarios. What you'll learn: - Understand core statistical concepts including mean, variance, probability distributions, and hypothesis testing. - Learn how to calculate sample sizes, formulate hypotheses, and set up controlled experiments. - Master key metrics such as p-values, confidence intervals, and statistical power to evaluate test results. - Identify common A/B testing pitfalls like novelty effects, sample ratio mismatch, and multiple testing issues. - Apply modern variance reduction concepts and statistical frameworks to improve experiment efficiency. The course begins with essential definitions and statistical terminology before guiding you step-by-step through test planning, execution, and data analysis. Through written readings and practical text walkthroughs, you will develop a structured framework for evaluating experimental data. This course is designed for aspiring data analysts, product managers, and decision-makers with no prior background in statistics required. Start reading today to turn raw experimental data into actionable insights.