So it was mentioned that one should ignore data driven models because this could inflate the type 1 error as a result of overfitting. So the model should be decided even before looking at the data set. But at the same time, other sources say we should look at the scatterplot matrix to determine if there is a confounder or not because confounders should be adjusted for in the regression model. Which should I do? Or is both involved in the process of data analysis?