![]() 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.
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