Nonparametric statistics refers to a branch of statistics that does not assume a specific distribution for the data. Unlike parametric methods, which rely on parameters like mean and standard deviation, nonparametric techniques can be used with data that do not meet these assumptions. This makes them particularly useful for analyzing ordinal data or data with outliers. Common nonparametric methods include the Wilcoxon signed-rank test and the Kruskal-Wallis test. These tests are often employed when sample sizes are small or when the data is skewed. Nonparametric statistics provide flexibility and robustness, making them valuable tools in various fields, including social sciences and biomedical research.