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> I'd give you links but without fail HN commenters just engage in ad hominem attacks on any source given without bothering to read them.

That's a red flag. Reads as evasive to me. Why are you bothered by internet randos disagreeing with you? If you have reputable sources link them instead of wasting time. If the responses are truly ad hominem that should only strengthen your argument in the eyes of any making an attempt to be objective.

I apologize if it seems uncharitable but my default assumption in this sort of scenario is that you've been the subject of well reasoned rebuttals and don't want to confront that reality.

> universal standards for what it calls science

> what kind of methods the word science really means

That's nonsensical. "Science" is a vague, very high level methodology. There's no single correct way to go about things other than to recursively apply evidence driven approaches.

Some fields have broad applicability of course. For example something like the foundations of statistical methods will presumably remain the same no matter what task you apply them to. But what the results of such methods "mean" is going to vary widely.



No, I've never seen a well reasoned rebuttal. The type of reply you get here on HN is always exactly like yours: an insistence on arguing the who instead of the what. Hence your requirement that any given sources are "reputable", without specifying what that means. Based on experience it's guaranteed to exclude 100% of all sources that discuss the issue honestly. That game has played out here 1000 times and is boring. If you disagree, check a fact or two. Go find sources that you think are reputable instead of expecting others to guess and read a few, poke holes in them if you can. That would be interesting.

> "Science" is a vague, very high level methodology. There's no single correct way to go about things

It's really not. But this is the kind of semantic dispute I am warning of. If your response to discovering that academics engage in data fabrication is to say that's ok because science can mean anything, then science doesn't mean anything. And eventually the general public will learn that, and vote to defund it.

> the foundations of statistical methods will presumably remain the same no matter what task you apply them to.

Overfitted models are not only common in many fields but academics often try to justify them as OK to use, so I wouldn't assume statistical methods remain the same no matter what task you apply them to. After all, science is really vague, right? Who is to say what the foundations of statistics are?




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