Randomization tests are statistical methods used to determine the significance of an observed effect by comparing it to a distribution of effects generated by random chance. In these tests, the data is shuffled or rearranged multiple times to create a null distribution, which represents the scenario where there is no real effect. This allows researchers to assess how extreme the observed effect is relative to what could occur by random variation. These tests are particularly useful in situations where traditional parametric tests may not be appropriate, such as when the data does not meet certain assumptions. By relying on randomization, these tests provide a flexible and robust way to evaluate hypotheses without requiring specific distributional assumptions, making them applicable in various fields, including psychology and biostatistics.