Rank-based tests are statistical methods used to analyze data without assuming a specific distribution. They focus on the ranks of the data rather than the actual values, making them robust against outliers and non-normal distributions. Common examples include the Wilcoxon signed-rank test and the Kruskal-Wallis test, which are often used for comparing two or more groups. These tests are particularly useful in situations where the sample sizes are small or when the data is ordinal. By converting data into ranks, rank-based tests provide a way to assess differences or relationships while minimizing the impact of extreme values.