Statistical Inference and A/B Testing for Data Analysis

Making business decisions based on raw data requires robust statistical reasoning to avoid costly false conclusions. Understanding statistical inference and experimental design enables you to transform raw metrics into actionable, evidence-based insights. This course guides you through the core principles of statistical inference, parameter estimation, and causal analysis. You will start with foundational statistical definitions and probability principles before exploring practical hypothesis testing, experiment design, and A/B testing methodologies. What you'll learn: - Understand foundational statistical concepts, probability distributions, and parameter estimation - Formulate and evaluate hypotheses using parametric and non-parametric statistical tests - Design robust controlled experiments and structure end-to-end A/B testing workflows - Distinguish between correlation and causation to establish clear causal relationships - Apply modern techniques such as bootstrap resampling and variance reduction to improve test sensitivity - Interpret p-values, confidence intervals, and effect sizes accurately in real-world scenarios The material begins with fundamental terminology and core statistical mechanics, smoothly transitioning into practical experiment design and modern statistical modeling patterns explained through structured reading and written code snippets. This foundational program is crafted for beginner data analysts, product analysts, and aspiring data scientists with no advanced statistics background required. Start reading today to build data-driven decision skills with complete confidence.