Do I need to correct for p-value when doing repeated ANOVA test? We are investigating the relationship between smoking status and the hormone level. Smoking status is X(three-level) and the level of the hormones is Y(continuous variable). However, the smoking status consists of three levels in our sample and there are 11 different types of hormone. I saw my friends did the repeated Kruskal-Wallis test (which is the non-parametric parallel to the ANOVA) for different hormone but make the adjustment to p-value using Benjamini-Hochberg procedure and set the number to 11. He explains to me that there are 11 different hormones but to my understanding, Benjaminin-Hochberg procedure is used to adjust for the multiple comparisons of three different smoking status.
I am not sure who is correct? Would it be valid to set Benjamini-Hochberg procedure to make adjustment assuming there are 11 different levels to compare? If not, what should we do to tackle the multivariate problems using non-parametric method (because the hormone level is not normally distributed)?
 A: One hormone. For each hormone, a one-way (one-factor) ANOVA or Kruskal-Wallis test will have three levels of the factor (groups for smoking status). First, you might do a test at the 5% level to see if there are any differences in hormone assays among the three groups. If that overall F-test shows no differences, you should not investigate further to find differences among groups. (Such further investigation would run an unusually high risk of 'false discovery'; you've already been "told" there are no legitimately significant differences.)
If the overall null hypothesis is rejected, suggesting that there are inter-group differences, you would typically do 'ad hoc' tests to determine the pattern of differences. A vs B, B vs C, and A vs C. You should use some method to avoid 'false discovery' in declaring differences among groups. One method (out of maybe a dozen in general use)
is the Bonferroni method ,which says these three 'ad hoc' tests
should be done at level 5%/3  = 1.7% level.
All 11 hormones. If you are doing individual ANOVAs for each of 11 hormones, you
need to be aware that testing each at the 5% level, you will have more than a 5% chance of falsely finding effects somewhere among the 11 ANOVAs--even if there are no homormonal changes
at all due to differences in smoking. 
Opinions differ as to how to mitigate this risk. From what you say in your question, I suppose you are planning to use the Benjamini-Hochberg procedure in this regard (one of several discussed in the Wikipedia link--near the end). It might be appropriate to do a multivariate ANOVA in which all 11 hormones are tested
simultaneously and the protection against false discovery is built into one overall test. 
