Which statistical test to use to test differences in multiple means (multiple populations) I have 3 populations, let's call them cluster 1, cluster 2 and cluster 3. The data are continuous. I want to see if there's a difference between the means of the three clusters. I know that the t-test tests for the difference of means, but that is only for 2 samples. What test should I use for multiple samples, i.e. 3, in my case?
 A: If you want a multi-group analog of a t-test it sounds like you just want  ANOVA (analysis of variance) or something similar to it. That's exactly what it's for - comparing group means.
Specifically, you seem to be asking for one-way analysis of variance.
Any decent statistics package does ANOVA. 
If you don't want to assume normality (just as you would for a t-test), there are a variety of options that still allow a test of means (including permutation tests and GLMs), but if your samples are large, moderate non-normality won't impact things much.
There's also the issue of potential heteroskedasticity; in the normal case many packages offer an approximation via an adjustment to error degrees of freedom (Welch-Satterthwaite) that often performs quite well. If heteroskedasticity is related to mean, you may be better off looking at an ANOVA-like model fitted as a GLM.
However, if the clusters are generated by performing cluster analysis on data, the  theory for t-tests, ANOVA, GLMs, permutation tests, etc no longer holds. None of the p-values would be correct
