Fisher's exact test
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Biostatistics and epidemiology
Biostatistics
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3 pages
![NOTES NOTES NON-PARAMETRIC TESTS NON-PARAMETRIC TESTS ▪ For data that is assumed to not be distributed normally ▪ For nominal/ordinal level variables CHI-SQUARED TEST osms.it/chi-squared_test ▪ Chi-square (𝜲2) goodness-of-fit test ▪ Test compares categorical variables ▫ Assesses for significant association ▪ Examines whether collected data is significantly different than theoretical model ▫ How “good is the fit” between data, what is expected ▪ Null hypothesis: no significant difference between theorized/expected, observed ratios ▫ 𝜲2 = sum of [(observed – expected)2/ expected] ▪ Use 𝜲2 table to find critical 𝜲2 ▫ Adjusted for degrees of freedom [n – 1], at selected p-value ▪ Accept null hypothesis if 𝜲2 < critical 𝜲2 CHI-SQUARE TEST OF INDEPENDENCE ▪ For analysis of contingency tables (or crosstabs tables) ▪ Investigates whether two/more categorical variables are statistically significant ▪ Used for multiple variables ▪ Degrees of freedom = (# of rows – 1) x (# of columns – 1) ▪ Requires > 5 data points in all cells of table; whole numbers ▪ Higher 𝜲2 results in lower p-value FISHER'S EXACT TEST osms.it/Fisher_exact_test ▪ Variant of chi-square test ▫ Used with small sample size ▪ Used to determine exact probability of association between two categorical variables (i.e. significance of association [contingency] between classifications) 62 OSMOSIS.ORG ▫ Use for 2 x 2 contingency tables (< 5 in a cell) ▫ p-values calculated exactly ▫ p < 0.05, unlikely to be random association](https://d16qt3wv6xm098.cloudfront.net/ZbMNfHyhTAWPM44U47yTx4h5T2C_RCBl/thumb-1.jpg)
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Summary
Fisher's exact test is a statistical test that uses a 2x2 categorical data design. It is used to determine whether the proportions of two categorical variables are different from each other. It is a more powerful alternative to the chi-squared test, and is particularly useful when there are small sample sizes.