F-test is any statistical test in which the test statistic has an F-distribution under the null hypothesis. In simpler terms F-Test is basically used to check the equality of two variances. F- Distribution on the other hand is drawn from the population and is used to check whether the two sample populations variance are homogenous or not.
F- test can be:
- Two-tailed – The two-tailed version tests against the alternative that the variances are not equal.
- One-tailed- The one-tailed version only tests in one direction that is the variance from the first population is greater than or less than (but not both) the second population variance.
F-test means the different between the samples.
Several assumptions one must keep in mind, before using F-test:
- The population must ne normally distributed, as N increase the population tends to be normal
- Sample must be independent.
F-Test is also used check the overall significance of the Regression model.
i.e. there is no significant difference between the intercept only model and the regression model calculated.
i.e. at least one of the B’s is significantly different from 0.
If the P value for the F-test test is less than your significance level, you can reject the null-hypothesis and conclude that your model provides a better fit than the intercept-only model.