In both G*Power and the R pwr package, estimated sample size required per group decreases as the number of groups increase. This seems somewhat counter-intuitive.
For a toy example, assume that I have two groups with a meaningful difference in their mean estimates (Group A and Group B). If I add several additional groups that have means identical to the grand mean of both groups (Groups C1, C2, C3, ...), the power analysis suggests smaller samples from Group A and Group B are needed -- which should make my ability to detect differences in those two groups weaker. At an extreme, if I enter 1500 levels of a single factor (f = .25, b = .8, a = .05), both programs effectively tell me to have group sizes of 2-3.
My understanding is that the power analyses from both programs helps you assess the power of the ANOVA overall. Thus, in the toy example, I'm more likely to pick up a difference between Group A or B and one of the Group Cs . However, this seems like it's a result of an increase in the number of comparisons and the likelihood that some of the Group C samples include mean estimates that are outliers. That doesn't seem like the type of difference I want to pick up.
What is the recommended approach in these circumstances -- or is the "low" sample size per group correct? Since I'm concerned with post-hoc comparisons of the group means, are there a priori power analyses available for those tests?