# How to Find the F Critical Value in Python?

In this article, we are going to learn about how to find the F Critical Value in Python.

## What is F Critical Value?

An F statistic is what you'll obtain after running an F test. Whether the results of the F test are statistically significant can be determined by comparing the F statistic to an F critical value. To put it simply, we compared our f value to the F-critical value as a standard. This post will look at a Python technique for finding the F critical value.

### Syntax

To calculate the F critical value, use the Python function scipy.stats.f.ppf(), which has the following syntax.

scipy.stats.f.ppf(q, dfn, dfd)

where

• Q implies the significance level to use
• Dfn implies the numerator degrees of freedom
• Dfd implies the denominator degrees of freedom

This function returns the important value from the F distribution based on the significance level, degrees of freedom for the numerator, and degrees of freedom for the denominator input.

As an example, suppose we want to get the F critical value with a significance level of 0.05, numerator degrees of freedom of 6, and denominator degrees of freedom of 8.

### Example

!pip3 install scipy
import scipy.stats

#find F critical value
scipy.stats.f.ppf(q=1-.05, dfn=6, dfd=8)


### Output

3.5805803197614603


A significance level of 0.05, and degrees of freedom in the numerator and denominator of 6, and 8 respectively, result in an F critical value of 3.5806.

Consequently, we can compare the F test statistic to 3.5806 if we're doing some kind of F test. The results of the test are considered statistically significant if the F statistic is higher than 3.580.

A lesser alpha value will result in a greater F critical value, so keep that in mind.

Think about the F critical value, for instance, at a significance level of 0.01, with degrees of freedom in the numerator and denominator both being 6.

### Example

!pip3 install scipy
import scipy.stats
#find F critical value
scipy.stats.f.ppf(q=1-.01, dfn=6, dfd=8)


### Output

6.370680730239201


## Conclusion

A crucial part of machine learning is statistics. Examining the raw data enables you to reach meaningful insights. For the machine learning model to utilize and make predictions based on, statistics uses the F critical value to identify the insights of the data.