d) Exact Tests and Approximate Tests

Exact Tests:
Exact tests are statistical procedures that yield exact p-values, providing precise probabilities of observing the obtained sample data or more extreme results under the null hypothesis. These tests are particularly useful when sample sizes are small or when the assumptions underlying traditional parametric tests, such as normality or homogeneity of variance, are violated. Unlike approximate tests, exact tests do not rely on asymptotic approximations, making them robust and reliable in various scenarios.

Examples of exact tests include:

1. Fisher's Exact Test: Fisher's Exact Test is employed to analyze contingency tables with small sample sizes, where traditional chi-square tests may be inappropriate due to the assumption of asymptotic distributions. It calculates the exact probability of observing the observed frequencies under the null hypothesis of independence between categorical variables.

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