Nonparametric statistics is a branch of statistics that deals with methods for analyzing data when the underlying distribution is not known or when the data does not meet the assumptions of parametric statistical tests. In nonparametric statistics, data is analyzed without making assumptions about the shape of the distribution or the parameters of the population from which the sample is drawn. Nonparametric methods are often used when the data is skewed, heavily-tailed, or has outliers, as well as in cases where the sample size is small. Examples of nonparametric statistical tests include the Wilcoxon signed-rank test, the Mann-Whitney U test, and the Kruskal-Wallis test.