Nonparametric 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, nonparametric methods are more flexible and can be applied to a wider range of data types, including ordinal and nominal data. They are particularly useful when the sample size is small or when the data does not meet the assumptions required for parametric tests. Common examples of nonparametric methods include the Wilcoxon signed-rank test, Kruskal-Wallis test, and Mann-Whitney U test. These methods focus on ranks or medians rather than means, making them robust against outliers and skewed distributions. Nonparametric methods are valuable tools in various fields, including psychology, medicine, and social sciences.