Intuitive A/B Testing and Hypothesis Testing

Making data-driven decisions should not require an advanced statistics degree. This course breaks down A/B testing and hypothesis testing into intuitive, practical concepts so you can evaluate product changes with complete confidence. You will gain a clear understanding of experimental logic and learn how to evaluate statistical significance without getting bogged down in heavy mathematical formulas. What you'll learn: Learn foundational concepts including null hypotheses, p-values, and confidence intervals. Design clean split tests that yield reliable and actionable business insights. Understand sample size needs, minimum detectable effect, and guardrail metrics intuitively. Identify common testing pitfalls like data peeking, false positives, and sample ratio mismatch. Interpret experiment results accurately to make confident product decisions. The course begins with foundational definitions and key vocabulary before walking through practical scenario analyses and decision frameworks. Designed for beginners, product managers, marketers, and aspiring analysts, no advanced mathematics or coding background is required. Start reading today to build your experiment intuition.