Usually, when doing multiple tests to compare means of categorical variables, it is advised to do some correction of the P-values to control the probability or proportion of false positives (Bonferroni, False Discovery Rate).
However, the definitions of these correction methods are so general that they seem to apply to any statistical test that yields a P-value.
Would it be make sense to correct P-values of tests that are comparing the value of continuous predictors to zero? In multiple regression models in ecology (generalized linear models, mixed effects models), we often test many predictors but never correct P-values explicitly. Is it relevant at all?