Personally, I perform Bonferroni corrections very rarely. If there are only few comparisons to be made,
then usually in my work there is one primary comparison, and some secondary comparisons, and I do
not mind making the secondary comparisons without Bonferroni (except a reviewer would demand it).
If there are many comparisons, Bonferroni destroys power and would therefore lead to a meaningless
analysis. But there certainly are circumstances, e.g. with a very large sample and/or the absolute priority
to prevent false-positive results, where Bonferroni (or preferably Bonferroni-Holm) can be justified.
I do not know whether you'd be better off with multiple/multivariate approaches such as multiple regression.
If you need to make 30 distinct comparisons, and all null hypotheses are true, then the probability of at least
one false-positive finding is about 80%. Instead of Bonferroni (0.05/30=0.0017) I would be inclined to choose
a conservative, but not so crazy significance level, such as 0.01. If all 30 null hypotheses were true, the chance
of at least one false-positve result would be 26% then. But if some null hypotheses were not true, you would
retain enough power with an alpha of 0.01.
Just my 2pence
Karabiner