Non-parametric Statistics

Non-parametric statistics refer to statistical methods that do not assume a specific distribution for the data. Unlike parametric methods, which rely on parameters like mean and standard deviation, non-parametric methods can be used with data that may not fit normal distributions or have outliers. They are often used for ordinal data or small samples. Examples include the Mann-Whitney U test and the Kruskal-Wallis test. Non-parametric statistics are valuable because they provide flexibility and robustness in analyzing various types of data without strict assumptions, making them widely applicable in different fields.