I recently encountered this question twice, on my exam. If you fit a full MLR additive, model, can you infer that the insignificant predictors (p-value < 0.05 from lm output) will not be chosen during stepwise regression, such as forward selection. I said no both times. My reasoning is that forward selection only looks at the partial contributions of each predictors after each model size, so sometimtes the non-significant predictors on a full model, might be significant on their own such that its picked by forward selection. So you can't reasonably infer that non-significant predictors will not be chosen by forward selection?