Do you need variance for t-test?
A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. To conduct a test with three or more means, one must use an analysis of variance.
Why is the homogeneity of variance assumption important in the t-test?
In conducting t tests and ANOVAs, the population variance (σ2) is estimated using sample data from both (all) groups. Based on the multiple groups, a pooled variance estimate of the population is obtained. The homogeneity of variance assumption is important so that the pooled estimate can be used.
Does the paired t-test assume homogeneity of variances?
The first one (Lehman, O’Rourke, Hatcher, & Stepanski, 2013) indicates explicitly that homogeneity of variance is an assumption for paired samples t-test (page 45). Page 188 explicitly said “The paired t–test does not assume that observations within each group are normal, only that the differences are normal.
Is the t-test necessary for homogeneity of variance?
No, it is not necessary. Given that there is a test that accounts for heterogeneous variances (Welch’s t-test), you can simply conduct it. For one, the tests for homogeneity of variance (HOV) are problematic in a number of ways.
When to use SPSS to test for homogeneity of variance?
When performing some statistical tests, SPSS routinely tests for homogeneity of variance. For example, if you perform an independent-measures t-test, SPSS will also show the results of a Levene’s test on the data.
Do you agree that assumption is tested not calculated?
Yes I agree, and I modified it, assumption is tested not calculated, My question is: Can we use the Levene’s test to test the assumption of homogeneity of variance in paired samples t-test ? Some argue that Levene’s test is only appropriate for independent samples. You are getting into a logical trap.
When is the assumption of homogeneity of variance biased?
If group sizes are vastly unequal and homogeneity of variance is violated, then the F statistic will be biased when large sample variances are associated with small group sizes. When this occurs, the significance level will be underestimated, which can cause the null hypothesis to be falsely rejected.