Non-parametric methods are statistical techniques that do not assume a specific distribution for the data. Unlike parametric methods, which rely on parameters like mean and variance, non-parametric methods can be applied to a wider range of data types, including ordinal and nominal data. This flexibility makes them useful in various fields, such as psychology, medicine, and social sciences. These methods include techniques like the Wilcoxon signed-rank test and Kruskal-Wallis test, which are used to compare groups without assuming normality. Non-parametric methods are particularly valuable when dealing with small sample sizes or when the data does not meet the assumptions required for parametric tests.