Non-Parametric Statistics: Mann-Whitney and Kruskal-Wallis Tests

When your research data does not meet the strict assumptions of normal distribution, how do you perform reliable hypothesis testing? Non-parametric tests are the essential tools every researcher, analyst, and academic exam candidate needs to master for analyzing ordinal or skewed data. This comprehensive text-only course provides a clear, step-by-step guide to understanding, calculating, and interpreting the Mann-Whitney U test and the Kruskal-Wallis H test. You will transition from basic statistical concepts to confidently selecting and applying the right non-parametric test for your research questions. What you'll learn: - Understand the fundamental differences between parametric and non-parametric statistical methods. - Identify when to apply the Mann-Whitney U and Kruskal-Wallis H tests based on your data structure. - Calculate test statistics step-by-step using ranking methods for two or more independent groups. - Interpret p-values, critical values, and significance levels to draw accurate research conclusions. - Report statistical findings clearly using standard academic and scientific reporting formats. - Recognize common pitfalls in non-parametric testing and how to avoid them. We begin with foundational definitions, data types, and the logic of rank-based testing. From there, you will read through practical, written scenarios that demonstrate calculations and interpretations without needing complex software. This course is designed for absolute beginners, university students, and researchers preparing for methodology exams who want a clear, conceptual understanding of non-parametric statistics. Start reading today to master these essential research tools and elevate your data analysis skills.