Distribution-free methods are statistical techniques that do not rely on assumptions about the underlying probability distribution of the data. This means they can be applied to a wide range of data types and are particularly useful when the data does not meet the assumptions required for traditional parametric tests. Examples of distribution-free methods include Wilcoxon signed-rank test and Kruskal-Wallis test. These methods are often used in situations where sample sizes are small or when the data is ordinal or not normally distributed. By focusing on the ranks or signs of the data rather than their specific values, distribution-free methods provide robust alternatives for hypothesis testing and data analysis.