Out-of-sample validation (validation of model performance on an independent set of data) is a must in physical modeling, data mining, etc., but in a traditional statistics course it is not taught and never (seldom?) practiced. I suspect that's connected with existence of a prescribed data model (which however is never tested either). Any thoughts on that?

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