# Fisher's exact test

Command: | Statistics Crosstabs Fisher's exact test |

## Description

If you have a 2x2 frequency table with small numbers of expected frequencies (in case the total number of observations is less than 20), you should not perform the Chi-squared test, but you should use *Fisher's exact test*.

## Required input

In the *Fisher's exact test* dialog box, two discrete dichotomous variables with the classification data must be identified. Classification data may either be numeric or alphanumeric (string) values. If required, you can convert a continuous variable into a dichotomous variable using the IF function (see elsewhere).

For example: in a study including 20 patients, 9 women and 11 men, the success of a treatment is recorded (1 = successful, 0 =no success). Is there a difference between the success rate in men and women?

The data are entered as follows in the spreadsheet:

The dialog box for the Fisher's exact test is completed as follows:

Option **Show all percentages**: see example below.

After you have completed the dialog box, click OK to obtain the frequency table with the relevant statistics.

## Classification table

The program displays the 2x2 classification table.

When you select the option **Show all percentages** in the dialog box, all percentages are shown in the table as follows:

In this example the number 1 in the upper left cell (for Classification X equal to "F" and Classification Y equal to 0) is 12.5% of the row total of 8 cases; 11.1% of the column total of 9 cases and 5.0% of the grand total of 20 cases.

## P-value

When the (two-sided) P-value (the probability of obtaining the observed result or a more extreme result) is less than the conventional 0.05, the conclusion is that there is a significant relationship between the two classification factors.

In the example P=0.028 and the conclusion therefore is that the success rate in men and women differs (or that the success rate is related to gender).

## Literature

- Altman DG (1991) Practical statistics for medical research. London: Chapman and Hall.