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I was just writing up an answer to this question when it was decided that it should be closed.

I understand why it is closed; the title alone makes it seem both a bit inflammatory and opinion based, which is generally not what we want on the site.

On the other hand, I feel like it brings up an especially important topic for statisticians; a machine learning type of approach (i.e., using cross-validation to tune hyper-parameters in overly parameterized models) seems to be doing very well at the task of prediction...so where does this leave the field of statistics?

The answer I was starting to write up was that

(1) If faced with the challenge of building a predictive model, I think statisticians should embrace what I'm calling the machine learning approach. After all, there are no hard lines between what's a statistical method and what's a machine learning method.

(2) There are a lot of very important tasks which can be addressed with statistics that have nothing to do with building black-box predictive models.

Anyways, I actually think it's a very interesting question that statisticians should think a little about, especially those early in their career. Do other users think this question could be fixed up or is it in general too open ended to belong on CV?

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Your drafted answer appears to be to a much more specific question; A question inspired by the actual one, rather than explicitly posed in it. Disquisitions on model checking, the logic of hypothesis testing, maximum-likelihood estimation under a mis-specified model, or causal inference would all seem equally germane.

You could help the OP to narrow down their question; or ask & answer your own.

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The question is opinion based and loaded. It isn't even clear if it is asked in good faith. At any rate, it is not our place to "fix up" the OP's question for them. The fixed-up version may not be what the OP wants. You can leave some comments to engage with the OP and see if you can get them to edit their question to the point where it would be acceptable.

Alternatively, as @Scortchi suggests, you could ask your own question, inspired by this one, that would be on topic and afford viable answers.

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