Fisher's Exact Test

Fisher's exact test is a test for independence between the row and column variables of a contingency table. When the data consist of two categorical variables, a contingency table can be constructed reflecting the number of occurrences of each factor combination. Fisher's exact test assesses whether the value of one factor is independent of the value of the other. For example, this might be used to test whether political party affiliation is independent of gender.

Certain types of homogeneity (for example, homogeneity of proportions in a k by 2 table) are equivalent to the independence hypothesis. Hence, this test may also be of interest in such cases.

As this is an exact test, the total number of counts in the cross-classification table cannot be greater than 200. In such cases, the chi-square test of independence is preferable.

To perform Fisher's exact test

Choose Statistics __image\ebd_ebd44.gif Compare Samples __image\ebd_ebd45.gif Counts and Proportions __image\ebd_ebd46.gif Fisher's Exact. The dialog shown below appears.

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The Fisher's Exact Test has the following options:

Data

Data Set

Specify a data set.

Variable 1

Specify the column from the data set which offers the first classification or grouping variable. This must be a factor or category.

Variable 2

Specify the column from the data set which offers the second classification or grouping variable. This must be a factor or category.

Data Set is a Contingency Table

Select if the data set specified is a contingency table.

Results

Save As

Enter the name for the object in which to save the results of the analysis.

Print Results

Select this to print out the results of the analysis in the designated output window.

Related S-PLUS language Functions

fisher.test, print.htest, menuFisher