What if homogeneity of variance is violated in ANOVA?

For example, if the assumption of homogeneity of variance was violated in your analysis of variance (ANOVA), you can use alternative F statistics (Welch’s or Brown-Forsythe; see Field, 2013) to determine if you have statistical significance.

What happens if ANOVA assumptions are violated?

If the populations from which data to be analyzed by a one-way analysis of variance (ANOVA) were sampled violate one or more of the one-way ANOVA test assumptions, the results of the analysis may be incorrect or misleading. A nonparametric test or employing a transformation may result in a more powerful test.

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.

What do you do when data fails test for homogeneity of variance?

So if your groups have very different standard deviations and so are not appropriate for one-way ANOVA, they also should not be analyzed by the Kruskal-Wallis or Mann-Whitney test. Often the best approach is to transform the data. Often transforming to logarithms or reciprocals does the trick, restoring equal variance.

When does ANOVA violate the homogeneity of variance assumption?

So the take-home message is this: With balanced data, ANOVA is generally robust to violations of the homogeneity of variance assumption (again, provided the ratio of the largest to smallest group variance is less than 4:1). However, this is not true with unbalanced data, as even relatively small differences in group variances can be problematic.

What to do when data fail tests for homogeneity of variance?

What to do when data fail tests for homogeneity of variance (part of one-way ANOVA)? One-way ANOVA assumes that the data come from populations that are Gaussian and have equal variances. GraphPad Prism tests this assumption with Bartlett’s test.

Is the t test sensitive to the assumption of homogeneity of variance?

Both t-test and ANOVA are sensitive to a violation of the assumption of homogeneity of variance. However, when group sample sizes are fairly equal, ANOVA remains robust in the event of small and even moderate departures from homogeneity of variance.

Which is a possible violation of one way ANOVA?

Some small violations may have little practical effect on the analysis, while other violations may render the one-way ANOVA result uselessly incorrect or uninterpretable. In particular, smallor unbalancedsample sizes can increase vulnerability to assumption violations. Potential assumption violations include: