Distribution-free methods

Distribution-free methods are statistical techniques used to analyze data without assuming it follows a specific probability distribution, like the normal (bell curve) distribution. These approaches are flexible and applicable even when the data's underlying pattern is unknown or does not fit common distributions. They often rely on rankings or data medians rather than raw values, making them robust for small or unusual datasets. Examples include the Mann-Whitney test and the Wilcoxon signed-rank test. Essentially, distribution-free methods provide reliable analysis without strict assumptions about the data's shape, making them versatile tools in diverse research scenarios.