I also came to stats through social sciences (now have a masters in stats). I was actually taught about stepwise regression in a stats course before I encountered it in social science.
The problems of stepwise are numerous. In my view many people confuse model fit and variable selection. All too often the analyst will use an automated approach for selecting variables that give the best model fit, without consideration for their meaning. It isn't uncommon to see missing or imbalanced categories. Almost none of them apply any corrections for multiple comparisons.
I encourage lasso over stepwise for variable selection and then fitting an OLS model if precise coefficient estimates are needed.
The problems of stepwise are numerous. In my view many people confuse model fit and variable selection. All too often the analyst will use an automated approach for selecting variables that give the best model fit, without consideration for their meaning. It isn't uncommon to see missing or imbalanced categories. Almost none of them apply any corrections for multiple comparisons.
I encourage lasso over stepwise for variable selection and then fitting an OLS model if precise coefficient estimates are needed.