Chi-Square Test vs. F Test

  • /
  • Blog
  • /
  • Chi-Square Test vs. F Test

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.


Customers served! 1

Quality Management Course

FREE! Subscribe to get 52 weekly lessons. Every week you get an email that explains a quality concept, provides you with the study resources, test quizzes, tips and special discounts on our other e-learning courses.

Similar Posts:

December 28, 2022

Two Parameters Weibull Distribution

December 19, 2022

What is Hypothesis Testing?

November 11, 2021

Negative Risks vs Positive Risks

December 24, 2021

Seven Quality Tools – Pareto Chart

December 26, 2022

Binomial Distribution

December 27, 2022

Geometric Distribution

December 18, 2022

Calculating the Range: A Quick Guide

December 27, 2021

Seven Quality Tools – Run Chart

December 21, 2022

Two Sample t Test (Independent Samples)

32 Courses on SALE!