Rank-based tests are statistical methods used to analyze data without assuming a specific distribution. They work by ranking the data points and then applying statistical techniques to these ranks rather than the original values. This makes them particularly useful for non-parametric data, where traditional tests may not be appropriate. Common examples of rank-based tests include the Wilcoxon signed-rank test and the Kruskal-Wallis test. These tests help determine if there are significant differences between groups or conditions while being less sensitive to outliers and skewed distributions. They are widely used in various fields, including psychology and medicine.