Mathematical Statistics and A/B Testing for Product Analysts

Are you struggling to make data-driven decisions based on experiment results? Understanding the fundamentals of statistical testing is crucial for product management, data analytics, and business intelligence roles. This course provides a complete, beginner-friendly introduction to statistical concepts, enabling you to design effective A/B tests, calculate necessary sample sizes, and accurately interpret results using both traditional and modern Bayesian approaches. What you'll learn: * Understand core statistical concepts: distributions, variance, standard deviation, and the central limit theorem. * Master A/B test design, including hypothesis formulation, sample size calculation, and power analysis. * Apply frequentist methods like p-values and confidence intervals to analyze conversions and averages. * Practice interpreting experiment results, understanding statistical significance, and avoiding common analytical pitfalls. * Learn the fundamental principles of Bayesian statistics and how they offer an alternative approach to decision-making. * Configure basic causal inference principles to reliably distinguish correlation from experimental causation. The course begins with essential terminology and probability theory before moving into practical applications of hypothesis testing. We then explore modern A/B testing design rigor and conclude with an introduction to Bayesian methods. This course is designed specifically for beginners—including aspiring data analysts, product managers, and business intelligence professionals—who need a practical foundation in statistics. No prior statistical knowledge is required. Start reading today and transform your ability to interpret data and run successful experiments.