Non-Parametric Statistics Learning With Ease

Course Title: Non-Parametric StatisticsThis course provides an in-depth exploration of non-parametric statistical methods, offering powerful tools for analyzing data without relying on assumptions of normality or equal variances. It begins with the definition and fundamental principles of non-parametric tests, emphasizing their advantages in handling ordinal data, small samples, and data with outliers. Learners will study the Sign Test, a simple yet effective method for testing the median of a distribution.

The course then introduces the Wilcoxon Signed-Rank Test, which serves as a non-parametric alternative to the paired t-test for comparing two related samples. The Mann-Whitney U Test is covered as a robust method for comparing two independent groups, while the Run Test is examined for testing the randomness of a data sequence. The course also includes the Kruskal-Wallis Test, which extends the Mann-Whitney test to more than two groups, making it a non-parametric alternative to one-way ANOVA.

To address the analysis of relationships between ranked variables, the course explores two key measures of association: Spearman's Rank Correlation Coefficient and Kendall's Tau Correlation Coefficient, both of which assess the strength and direction of monotonic relationships between variables. Throughout the course, practical examples and real-world applications are emphasized to ensure learners can confidently apply non-parametric techniques to various research and professional scenarios.