Chi-Square Test vs. F Test

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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).

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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.

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