Chi-Square Test vs. F Test

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

Purpose:

These two statistical procedures are used for different purposes.

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The chi-square test is used when you want to know whether there is a statistical difference between two categorical variables (e.g., gender and preferred car color).

The F test, on the other hand, 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 assumption 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 from one another.

Types of Tests:

Chi square test:
- For testing the population variance against a specified value
- For testing goodness of fit of some probability distribution
- Testing for independence of two attributes (Contingency Tables)

F test
- For testing equality of two variances from different populations
- For testing equality of several means with technique of ANOVA.