ABSTRACT

This study provides an exploration of non-parametric statistical tests, focusing on their historical development, theoretical foundations, and practical applications. Three key non-parametric tests - the Mann-Whitney U test, Wilcoxon signed-rank test, and Kruskal-Wallis test are examined in detail through case studies involving real-world datasets. These tests prove valuable in various fields, including medicine, biology, social sciences, and environmental studies, particularly when dealing with non-normal data distributions, small sample sizes, and ordinal or categorical data. The study also highlights future research directions, such as the integration of non-parametric and parametric methods, robustness to assumption violations and outliers, and the adaptation of non-parametric methods for big data analysis. Overall, the project underscores the importance of non-parametric tests as robust alternatives to traditional parametric methods, especially when dealing with real-world data that may not conform to standard assumptions.