My binary logistic regression gives P values significant at 0.05 level for some of the modeled independent variables. However, bootstrapping the same regression model gives P values significant at 0.01 or 0.001 levels for the same predictors which were mostly significant only at 0.05 level, or even for some of them which were not significant!
Does it have a worthwhile meaning? A huge discrepancy between the two P values of the same variable worries me, especially when I see the bootstrapped one that I expected to give more conservative results is giving much stronger results! I read somewhere that in such a case, we should discuss that the results pertaining to these variables should be assessed further. But there were no explanations and such a recommendation doesn't seem so specific.
Which P value is better here? Are both correct? (especially the bootstrapped one).
Do you have any recommendations for this strange P value behavior?