I found in multiple sources the recommendation not to fit the normalization parameters on the combined train/test dataset when evaluating the model, to prevent data leakage.
I presume this recommendation assumes prediction data would flow to the model online, and the entire data is not known in advance.
Does this recommendation also apply to when the entire data is known in advance? And even if not, I guess it is possible to refit the model every time we get new data, with normalization parameters acheived from the entire dataset seen until now?
What are the pros and cons of normalizing over the entire dataset before fitting (as opposed to only on the train set)?