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Why is the assumption of homogeneity of variance important to independent samples t tests?
Homogeneity of variance essentially makes sure that the distributions of the outcomes in each group are comparable and similar. If independent groups are not similar in this regard, superfluous findings can be yielded.
What is the assumption of homogeneity of variance?
Homogeneity of variance is an assumption underlying both t tests and F tests (analyses of variance, ANOVAs) in which the population variances (i.e., the distribution, or “spread,” of scores around the mean) of two or more samples are considered equal.
Can you still use the independent measures t test if the homogeneity of variance assumption is violated?
Because we assume equal population variances, it is OK to “pool” the sample variances (sp). However, if this assumption is violated, the pooled variance estimate may not be accurate, which would affect the accuracy of our test statistic (and hence, the p-value).
What if Levene’s test is significant in independent t test?
If the Levene’s Test is significant (the value under “Sig.” is less than . 05), the two variances are significantly different. If it is not significant (Sig. is greater than . 05), the two variances are not significantly different; that is, the two variances are approximately equal.
How is the assumption of homogeneity of variance tested?
To test for homogeneity of variance, there are several statistical tests that can be used. These tests include: Hartley’s F max, Cochran’s, Levene’s and Barlett’s test. Several of these assessments have been found to be too sensitive to non-normality and are not frequently used.
What is the assumption of normality in the independent t test?
The test (dependent) variable is normally distributed within each of the two populations (as defined by the grouping variable). This is commonly referred to as the assumption of normality. The variances of the test (dependent) variable in the two populations are equal. This is commonly referred to as the assumption of homogeneity of variance.
What are the assumptions in a two sample t test?
A two sample t-test makes the assumption that both samples are approximately normally distributed. This is a crucial assumption because if the samples are not normally distributed then it isn’t valid to use the p-values from the test to draw conclusions about the differences between the samples.
When to use a t test in statistics?
The T-Test. A t-test is an analysis of two populations means through the use of statistical examination; a t-test with two samples is commonly used with small sample sizes, testing the difference between the samples when the variances of two normal distributions are not known. T-distribution is basically any continuous probability distribution…