Repeated Measures ANOVA and Non-Parametric Statistics

When analyzing experimental data, tracking the same subjects over time or under multiple conditions requires specialized statistical methods. Standard independent tests fall short when measurements are dependent, and normal distribution assumptions do not always hold. This text-based course guides you through the core principles of within-subjects designs, helping you transition from basic t-tests to advanced repeated measures ANOVA and non-parametric alternatives. You will gain the practical skills to select, run, and interpret the right statistical tests for paired and ordinal data. What you'll learn: - Understand the fundamental logic and advantages of within-subjects experimental designs - Compare paired sample t-tests and repeated measures ANOVA to analyze changes over time - Identify when statistical assumptions are violated and require alternative approaches - Apply non-parametric tests like the Wilcoxon signed-rank and Kruskal-Wallis tests - Structure and format dependent data sets for modern statistical analysis software - Interpret statistical outputs accurately to draw valid, data-driven conclusions The course starts with essential terminology and foundational concepts of dependency in data before walking you step-by-step through parametric and non-parametric testing methodologies. You will practice through written scenarios, conceptual exercises, and step-by-step analytical walkthroughs. This course is designed for beginner data analysts, researchers, and students who want to expand their statistical toolkit without needing prior advanced mathematical training. Start mastering repeated measures and non-parametric analysis today.