# What is the difference between Chi-square test and F Test?

There are two main types of variance tests: **chi-square tests** and **F tests.**

### Purpose:

These two statistical procedures are used for different purposes.

The** chi-square test** is used to determine whether there is a statistical difference between two categorical variables (e.g., gender and preferred car colour).

On the other hand, the **F test** is used when you want to know whether there is a statistical difference between two continuous variables (e.g., height and weight).

### Distribution:

The **chi-square test** is non-parametric. That means this test does not make any assumptions about the distribution of the data.

The **F test** is a parametric test. It assumes that data are normally distributed and that samples are independent of one another.

### Types of Tests:

**Chi-square test:**

- For testing the population variance against a specified value

- For testing the goodness of fit of some probability distribution

- Testing for the independence of two attributes (Contingency Tables)**F test**

- For testing the equality of two variances from different populations

- For testing the equality of several means with the technique of ANOVA.