Non-parametric tests are statistical methods used to analyze data that do not assume a specific distribution, such as the normal distribution. These tests are particularly useful when dealing with small sample sizes or ordinal data, where traditional parametric tests may not be appropriate. Examples of non-parametric tests include the Mann-Whitney U test and the Kruskal-Wallis test. These tests focus on the ranks or signs of the data rather than their actual values, making them more flexible in handling various types of data. Non-parametric tests are often easier to apply and interpret, especially when the underlying assumptions of parametric tests are violated.