🚀 go-pugleaf

In a somewhat-related following question to my recent posting concerning code
for Box-Cox transformations, recent discussions with some statistical types
suggests that the following might be of general interest.

Suppose you want to do a simple analysis, say a single-classification
anova. There are 5 levels of the classification variable. You do
whatever homogeneity of variance/normality tests you subscribe to, and you
find that the data is either/or heteroscedastic or non-normal.

However, you also notice that no SINGLE transformation can be applied to
all groups. I.E maybe x^2 works for data in one group, while log(x)
works for another. Alternatively, the "best" Box-Cox transform differes
among groups.

Question: If you can't use the same transform for all your data, should
          you transform at all?

Funny how all the intro biometry texts talk about transformations, but none
of them that I've seen address this.

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