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Use Fisher's exact test when analyzing a 2x2 contingency table with small sample sizes or when expected cell frequencies are less than five. Unlike the chi-square test, which approximates P-values and requires minimum expected frequencies of at least five, Fisher's exact test calculates the exact probability of observing your data under the null hypothesis, ensuring accurate results even with sparse data. This makes it part of introduction to nonparametric statistics methods.

A small P-value indicates that the observed relationship between two categorical variables is statistically significant and unlikely to occur by chance. For example, when comparing drug efficacy, a small P-value suggests the difference in success rates between treatments is statistically significant, supporting the conclusion that the treatments differ meaningfully in their effectiveness.

Fisher's exact test is non-parametric because it does not assume the data follows any specific probability distribution. Instead of relying on distributional assumptions like parametric tests do, it calculates exact probabilities based directly on the observed contingency table, making it robust and reliable regardless of how the underlying data is distributed.

Fisher's exact test analyzes categorical data represented in a 2x2 contingency table where both variables are categorical. It is widely used in medicine to evaluate treatment effects in small clinical trials, in biology to analyze genetic associations or experimental outcomes, and in social sciences to examine relationships between categorical variables.

Fisher's exact test calculates the exact P-value by computing all possible contingency tables with the same marginal totals as the observed data. This approach effectively handles unequally distributed data and small sample sizes by determining the precise probability of observing results as extreme or more extreme than those observed, ensuring precision regardless of data distribution patterns.

Fisher's exact test yields an exact P-value rather than an approximation, ensuring precision and integrity in result interpretation. Its exactness makes it especially valuable when sample sizes are small or expected cell counts are low, where approximation methods like chi-square may fail or provide unreliable outcomes, making it a robust alternative for researchers working with limited datasets.

Fisher's exact test is applied across medicine, biology, and social sciences. In medical research, it evaluates treatment effectiveness and safety in small clinical trials. In biology, it analyzes genetic associations and experimental outcomes. In social sciences, it examines relationships between categorical variables, making it a versatile tool for assessing statistical significance when traditional approaches are unsuitable.