Nonparametric tests are statistical methods that do not assume a specific distribution for the data. Unlike parametric tests, which rely on parameters like mean and standard deviation, nonparametric tests focus on the ranks or order of the data. This makes them useful for analyzing data that is ordinal or not normally distributed. These tests are often used when sample sizes are small or when the data contains outliers. Common examples of nonparametric tests include the Mann-Whitney U test, Kruskal-Wallis test, and Wilcoxon signed-rank test. They provide a flexible approach to hypothesis testing without strict assumptions about the underlying data.