Non-parametric methods are statistical techniques 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 do not fit traditional distributions. This makes them versatile for analyzing various types of data, including ordinal and nominal scales. Common non-parametric tests include the Mann-Whitney U test and the Kruskal-Wallis test, which are used to compare groups without assuming normality. These methods are particularly useful when sample sizes are small or when data contain outliers that could skew results in parametric tests.