Exact tests are statistical methods used to determine the significance of results when sample sizes are small or when data does not meet the assumptions of traditional tests. These tests provide precise p-values, allowing researchers to make reliable inferences about their data without relying on large-sample approximations. One common example of an exact test is the Fisher's Exact Test, which is often used for analyzing contingency tables. This test is particularly useful in clinical trials or studies with rare events, where the number of observations is limited, ensuring that conclusions drawn are valid and not influenced by sample size.