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Fisher’s Exact Test is used to determine whether or not there is a significant association between two categorical variables.

It is typically used as an alternative to the Chi-Square Test of Independence when one or more of the cell counts in a 2×2 table is less than 5.

This tutorial explains how to perform Fisher’s Exact Test in Python.

**Example: Fisher’s Exact Test in Python**

Suppose we want to know whether or not gender is associated with political party preference at a particular college.

To explore this, we randomly poll 25 students on campus. The number of students who are Democrats or Republicans, based on gender, is shown in the table below:

Democrat | Republican | |
---|---|---|

Female | 8 | 4 |

Male | 4 | 9 |

To determine if there is a statistically significant association between gender and political party preference, we can use the following steps to perform Fisher’s Exact Test in Python:

**Step 1: Cr****eate the data.**

First, we will create a table to hold our data:

data = [[8, 4], [4, 9]]

**Step 2: Perform Fisher’s Exact Test.**

Next, we can perform Fisher’s Exact Test using the fisher_exact function from the SciPy library, which uses the following syntax:

**fisher_exact(table, alternative=’two-sided’) **

where:

**table:**A 2×2 contingency table**alternative:**Defines the alternative hypothesis. Default is ‘two-sided’, but you can also choose ‘less’ or ‘greater’ for one-sided tests.

The following code shows how to use this function in our specific example:

import scipy.stats as stats print(stats.fisher_exact(data)) (4.5, 0.1152)

The p-value for the tests is **0.1152**.

Fisher’s Exact Test uses the following null and alternative hypotheses:

**H**The two variables are independent._{0}: (null hypothesis)**H**The two variables are_{1}: (alternative hypothesis)*not*independent.

Since this p-value is not less than 0.05, we do not reject the null hypothesis.

Thus, we don’t have sufficient evidence to say that there is a significant association between gender and political party preference.

In other words, gender and political party preference are independent.