Nonparametric methods are a set of statistical techniques that do not rely on specific distributional assumptions about the data being analyzed. Instead of assuming a specific functional form for the data, nonparametric methods use rank-based or data-driven approaches to make inferences. These methods are often used when the underlying data does not meet the assumptions of traditional parametric tests or when the sample size is too small to assume normality. Nonparametric methods are widely used in fields such as biology, psychology, sociology, and environmental science. Some common nonparametric tests include the Mann-Whitney U test, Kruskal-Wallis test, Wilcoxon signed-rank test, and Spearman's rank correlation.