Rank-based methods are statistical techniques that focus on the order of data rather than their specific values. These methods are particularly useful when dealing with non-normally distributed data or when outliers may skew results. By ranking the data points, researchers can analyze relationships and differences without being influenced by extreme values. Common applications of rank-based methods include the Wilcoxon signed-rank test and the Kruskal-Wallis test. These tests help determine if there are significant differences between groups or conditions based on the ranks of the data. Overall, rank-based methods provide a robust alternative to traditional parametric tests.