Does the sequential Bonferroni correction method apply to my data?

The literature on Bonferroni correction methods is confusing me...

I have compared means of multiple variables (some dependent, some independent) with Fisher's exact tests, Kruskal-Wallace tests, and Mardia-Watson-Wheeler tests. All in an attempt to identify important variables in the presence or absence of a species. Should I use the sequential Bonferroni correction now on all of my p-values?

It appears based on the literature that this may be useful since the high number of variables I have measured may increase the "incorrect" probability of getting a significant P-value in one of my variables. However, I am not sure if the "multiple tests" that the literature keeps referencing is referring to multiple statistical tests on one data set, i.e. temperature affecting presence/absence; or instead the multiple variables I have measured in order to predict presence/absence of a species, i.e. temperature, elevation, distance to water, etc.

I only used one statistical test for each variable.

So, in summary: is it logical to now use the sequential Bonferroni correction method on my list of P-values that are associated with different variables? (I am using R if this is relevant to any answers!)

Thank you so much in advance for your patience!
Confused & frustrated grad student.


Less is more. Stay pure. Stay poor.
Please provide a little bit more about your analyses and what you are doing, perhaps with content. Are you just trying to combat your multiple analytic approaches since you may have had a little bit of a fishing expedition?

Are you referring to using a Holm-Bonferroni correction?