Compared to the normal distribution, a distribution with heavy tails has more data at its lower and upper ends. The distribution for one or more of the populations is extremely skewed or has heavy tails.Your samples have less than 20 observations each.You should base your conclusions on the results for the multiple comparisons method, unless the following are true: If the p-value for the multiple comparisons method is significant, then you can use the summary plot to identify specific populations that have standard deviations that are different from each other. The multiple comparisons method is usually more powerful. For most continuous distributions, both methods give you a type I error rate that is close to your specified significance level (also known as alpha or α). You can feel confident that the assumption of equal variances is being met.īy default, Minitab's Test for Equal Variances command displays results for Levene's method and the multiple comparisons method.
![two sample unequal variance t test two sample unequal variance t test](https://i.ytimg.com/vi/dvNfw4PghDw/hqdefault.jpg)
If the resulting p-value is greater than adequate choices of alpha, you fail to reject the null hypothesis of the variances being equal. Because this unbalanced condition increases the susceptibility to unequal variances, you decide to test the assumption of equal variances. You use the ANOVA general linear model (GLM) because you have unequal sample sizes. Alternatively, ANOVA models with random effects and/or unequal sample sizes could be substantially affected.įor example, you plan to do an ANOVA testing the length of time callers are put on hold where the main fixed factor is the calling center. For example, ANOVA inferences are only slightly affected by inequality of variance if the model contains only fixed factors and has equal or almost equal sample sizes.
![two sample unequal variance t test two sample unequal variance t test](https://i.ytimg.com/vi/0XSYh-VY9M4/maxresdefault.jpg)
Many statistical procedures, such as analysis of variance (ANOVA) and regression, assume that although different samples can come from populations with different means, they have the same variance.īecause the susceptibility of different procedures to unequal variances varies greatly, so does the need to do a test for equal variances. Use a test for equal variances to test the equality of variances between populations or factor levels.