I'm not sure if this is "step-wise" model selection, but here is what I'm doing
- Decide a handful of models through exploratory data analysis.
- Fit the models to the data, and calculate their AIC.
- Pick the model with the lowest AIC score.
I've read that step-wise AIC is "primarily problematic for inference", but don't understand the rationale behind this claim. While I know there are differences between inference and prediction, I'm unaware of the restrictions they put on analytical procedures.
Is my model selection procedure problematic for inference? for prediction? Why?