If I have several hundred coefficients generated by running multiple variable regression model (keeping it as broad as possible by not specifying the nature of the predictors and outcome variable), it would appear to me that I have two options for assessing the significance of any one result.
Identify the largest standardized predictors by calculating z-scores for regression coefficients and assessing how many exceed a fixed threshold (e.g. 2 Z-scores from 0).
Use permutation testing to simulate the distribution of covariates under a null hypothesis (of no covariates being associated with the outcome) and calculate a p-value by comparing the actual results, ranked by their order of significance calculated from Z-scores, to the distributions of ranked coefficients in the permutation tests
My question is: What are the advantages and disadvantages of each approach?