Description: Fisher’s Exact Test is a statistical technique used to determine the significance of the association between two categorical variables in a contingency table. Unlike other statistical tests, such as the chi-square test, which require a sufficiently large sample size to be valid, Fisher’s Exact Test is particularly useful in situations where expected frequencies are low. This test calculates the probability of observing a data distribution as extreme as the one observed under the null hypothesis that there is no association between the variables. Its approach is based on combinatorial principles, allowing for precise results without relying on approximations. Fisher’s Exact Test is particularly valued in fields such as biology, medicine, and social sciences, where data may be scarce or uneven. Its ability to handle 2×2 contingency tables makes it an essential tool for researchers seeking to establish significant relationships between categorical variables, providing a solid foundation for informed decision-making in empirical studies.
History: Fisher’s Exact Test was developed by British statistician Ronald A. Fisher in 1922. Fisher introduced this test in his work on statistical inference, seeking a way to analyze categorical data in experiments where samples were small. His innovative approach was based on probability theory and combinatorics, allowing for the exact calculation of the probabilities of observed distributions. Since its introduction, the test has evolved and become a fundamental tool in applied statistics, especially in biomedical studies and social sciences.
Uses: Fisher’s Exact Test is primarily used in studies analyzing two categorical variables, especially in situations with small samples or when expected frequencies are low. It is common in medical research to assess the effectiveness of treatments, in epidemiological studies to analyze the relationship between risk factors and diseases, and in social sciences to investigate the association between demographic variables. Its ability to provide precise results makes it indispensable in scientific research.
Examples: A practical example of Fisher’s Exact Test could be a study evaluating the relationship between treatment with a new drug and the recovery of patients with a specific disease. If there is a group of 20 patients, where 10 received the drug and 10 a placebo, and it is observed that 8 out of 10 treated patients recovered compared to 2 out of 10 who received the placebo, the test can determine if this difference is statistically significant. Another example could be an analysis of consumer preference between two brands of a product, where responses are categorized as ‘favorite’ or ‘not favorite.’
