What is F-Test?
The F test is a statistical technique that determines if the variances of two samples or populations are equal using the F test statistic. Both the samples and the populations need to be independent and fit into an F-distribution. The null hypothesis can be rejected if the results of the F test during the hypothesis test are statistically significant; if not, it stays unchanged.
We can use this test when:
- The population is normally distributed.
- The samples are taken at random and are independent samples.
F-Test Formula using two covariances
Here,
- Fcalc = Critical F-value.
- σ12 & σ22 = variance of the two samples.
Here,
- df = Degrees of freedom of the sample.
- nS = Sample size.
F-Test in Statistics
F test is a statistical test that is used in hypothesis testing, that determines whether or not the variances of two populations or two samples are equal. An f distribution is what the data in a f test conforms to. By dividing the two variances, this test compares them using the f statistic. Depending on the details of the situation, a f-test can be one-tailed or two-tailed. The article will provide further information on the f test, the f statistic, its calculation, critical value, and how to use it to test hypotheses.
Table of Content
- F-distribution
- What is F-Test?
- Hypothesis Testing Framework for F-test
- Example Problem for calculating F-Test
- Frequently Asked Questions (FAQs)