Fisher's Exact Test is a statistical method used to determine if there are nonrandom associations between two categorical variables in a contingency table. It is particularly useful when sample sizes are small, making traditional tests like the Chi-squared test less reliable. The test calculates the exact probability of observing the data, assuming the null hypothesis of no association is true. The test works by evaluating all possible outcomes of the data, providing an exact p-value. This p-value helps researchers decide whether to reject the null hypothesis, indicating a significant relationship between the variables. It is commonly used in fields like medicine and social sciences.