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?
> "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?