Why is it common practice to take the log of the dependent variable Y? To be clear, I understand that under appropriate circumstances that taking the log can help normalize the distribution/linearize the model and I have read other threads discussing this. What I am confused about is why is it okay to transform and normalize/linearize Y and make it falsely 'appear' normal instead of using the real, raw data? Don't we want to train the X variable(s) to be able to predict/determine Y as it is, so why are we altering Y?
Additionally, when exactly in the modeling process do we do this? Would I log Y in the linear model lm(log(Y) ~ X, data = df)? Would I do it when calculating RMSE? Thanks in advance!