Should I use equal or UNequal variance?
In practice, one usually doesn’t know whether or not population variances are equal. So good statistical practice is to use the Welch version of the two-sample t test, unless one has reliable prior evidence that population variances are equal. Note: The F-test for unequal variances has poor power.
What is the difference between t test equal variance and UNequal variance?
The Two-Sample assuming Equal Variances test is used when you know (either through the question or you have analyzed the variance in the data) that the variances are the same. The Two-Sample assuming UNequal Variances test is used when either: You know the variances are not the same.
How to compare the variance of two variables?
To compare the variances of two quantitative variables, the hypotheses of interest are: The last two alternatives are determined by how you arrange your ratio of the two sample statistics. We will rely on Minitab to conduct this test for us. Minitab offers three (3) different methods to test equal variances.
How to do a F test of two variances?
A supermarket might be interested in the variability of check-out times for two checkers. In order to perform a F test of two variances, it is important that the following are true: The populations from which the two samples are drawn are normally distributed. The two populations are independent of each other.
Which is the best test for equality of variances?
This F-test is known to be extremely sensitive to non-normality, so Levene’s test, Bartlett’s test, or the Brown–Forsythe test are better tests for testing the equality of two variances.
How to test the null hypothesis of equality of variances?
The expected values for the two populations can be different, and the hypothesis to be tested is that the variances are equal. Let be the sample means. Let be the sample variances. Then the test statistic has an F-distribution with n − 1 and m − 1 degrees of freedom if the null hypothesis of equality of variances is true.