According to Keppel (1993), there is no good rule of thumb for how unequal the sample sizes need to be for heterogeneity of variance to be a problem. But that’s not true when the sample sizes are very different. The main practical issue in one-way ANOVA is that unequal sample sizes affect the robustness of the equal variance assumption.ĪNOVA is considered robust to moderate departures from this assumption.

Assumption Robustness with Unequal Samples Two Practical Issues for Unequal Sample Sizes in One-Way ANOVA 1. They don’t invalidate an analysis, but it’s important to be aware of them as you’re interpreting your output. That’s not a big deal if you’re aware of it.īut there are a few real issues with unequal sample sizes in ANOVA. Instead of the grand mean, you need to use a weighted mean. Nice properties in ANOVA such as the Grand Mean being the intercept in an effect-coded regression model don’t hold when data are unbalanced. Sums of squares require a different formula* if sample sizes are unequal, but statistical software will automatically use the right formula.

Because she was making you calculate everything by hand. In your statistics class, your professor made a big deal about unequal sample sizes in one-way Analysis of Variance (ANOVA) for two reasons.ġ.

How to do an anova in excel 2007