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Fisher exact test
58%
1881/3260
Regression analysis
3%
101/3260
Two-sample t-test
15%
491/3260
Mann-Whitney test
11%
354/3260
Analysis of variance (ANOVA)
13%
413/3260
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In the situation where there are relatively few total cases (the sum of all cells is less than approximately 50 or less than 5 events in a cell), the Fisher exact test is the most appropriate substitute for the chi-square test. Fisher's exact test is a statistical significance test used in the analysis of contingency tables. Although in practice it is employed when sample sizes are small, it is valid for all sample sizes. It is one of a class of exact tests, so called because the significance of the deviation from a null hypothesis (e.g., P-value) can be calculated exactly, rather than relying on an approximation that becomes exact in the limit as the sample size grows to infinity, as with many statistical tests. The chi-square test is a simple method of comparing two proportions, such as a difference in nonunion rates(%) between two groups of fracture patients. The two-sample t-test is a parametric test that compares two means and the Mann-Whitney test is a non-parametric test that compares two means. Analysis of variance (ANOVA) compares one dependent variable among three or more groups. Regression analysis is used to estimate the association between a response variable and a series of known explanatory variables (includes simple, multiple, and logistic regression). The article by Kuhn et al reviews the basics of statistical inference.
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