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 biology, social sciences, and economics. Common non-parametric methods include the Wilcoxon signed-rank test, Kruskal-Wallis test, and Mann-Whitney U test. These methods are particularly valuable when sample sizes are small or when the data do not meet the assumptions required for parametric tests. They help researchers draw conclusions without the constraints of traditional statistical models.