Fisher's Exact Test
Fisher's Exact Test is a statistical significance test used to analyze categorical data in contingency tables, particularly for small sample sizes. It calculates the exact probability of observing a given distribution of frequencies, assuming the null hypothesis of independence between variables. It is commonly applied in fields like biology, medicine, and social sciences to test associations in 2x2 tables.
Developers should learn Fisher's Exact Test when working on data analysis, machine learning, or research projects that involve categorical data with small sample sizes, as it provides accurate p-values without relying on large-sample approximations. It is especially useful in A/B testing, bioinformatics (e.g., gene association studies), and survey analysis where data may be sparse or imbalanced, ensuring robust statistical inference.