What happens if homogeneity of variance is violated

Analyze, graph and present your scientific work easily with GraphPad Prism. No coding required. Home Support. It is not really clear what to do next. Here are some thoughts: This gives you strong evidence that the groups are not selected from identical populations. You haven't yet tested whether the means are distinct, but you already know that the variances are different. This may be a good stopping point. You have strong evidence that the populations the data are sampled from are not identical.

Some statisticians suggest never using Bartlett's test. Can anyone help me out here? I would be very glad. In a t-test, for example, if your variances are unequal, this can affect the Type I error rate i. The way in which the significance level is affected can depend on group size. If large variances are associated with smaller groups, significance will be underestimated, which may mean that the null is falsely rejected.

If large variances are associated with larger groups, significance would be overestimated. Sign up to join this community. The best answers are voted up and rise to the top. Stack Overflow for Teams — Collaborate and share knowledge with a private group.

Create a free Team What is Teams? Learn more. How does the violation of the homogeneity of variance assumption affect statistical test results? Ask Question. Asked 1 year, 9 months ago. In ANOVA, when homogeneity of variance is violated there is a greater probability of falsely rejecting the null hypothesis. In regression models, the assumption comes in to play with regards to residuals aka errors.

This tutorial serves as an introduction to assessing the assumption of homogeneity. First, I provide the data and packages required to replicate the analysis and then I walk through the ways to visualize and test your data for this assumption. To illustrate ways to visualize homogeneity and compute the statistics, I will demonstrate with some golf data provided by ESPN. The golf data has 18 variables, you can see the first 10 below. Scatter plots are a useful way to look at the variance of a data and are, typically, our first step in assessing homogeneity.

We can illustrate with some golf data provided by ESPN. Here we are assessing the number of birdies players score versus the rank of the player fig 1 and the number of events played fig 2.

For both figures I added the trend line which makes it easier to assess the variance or spread of points. In figure 1 it appears that the dots are spread out fairly evenly across the line; this is what is meant by homogeneity of variance.