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PhilGoetz comments on Even if you have a nail, not all hammers are the same - Less Wrong
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I don't understand. Why is "they used the wrong statistical formula" worth 47 upvotes on the main article? Because people here are interested in supplementation? Because it's a fun math problem?
In the other comments, people are discussing which algorithm would be more appropriate, and debating the nuances of each particular method. Not willing to take the time to understand the math, it comes across as, "This could be right, or wrong, depending on such-and-such, and boy isn't that stupid..."
I run into this problem every time I read anything on health or medicine (it seems limited to these topics). Someone says it's good for you, someone says it's bad for you, both sides attack the other's (complex, expert) methods, and the non-expert is left even more confused than when they first started looking into the matter. And it doesn't help that personal outcomes can be drastically different regardless of the normal result.
To me, this topic is still confusing, with a slight update toward "take more vitamins." Without taking classes in statistics and/or medicine, how can I become less wrong on problems like this? Who can I trust, and why?
You can't learn to be less wrong about mathematical questions without learning more math. (By definition.)
Depends. I could become less wrong about mathematical questions by learning to listen to people who are less wrong about math. (More generally: I may be able to improve my chance of answering a question correctly even if I can't directly answer it myself.)
The "problem like this" I was referring to was "health advice and information is often faulty," not "linear regression analysis of mortality effects from supplementation is faulty."
I'd like to get better at correcting for the former while avoiding the (potentially enormous) amount of learning and effort involved in getting better at all necessary forms of the latter.
From what I can tell, you're saying "there is no way; the two are inextricably linked." In which case, I guess I'll just wait until they get better at it.
The general advice here is
All of those points are true, but there's one I'd like to flag as true but potentially misleading:
Linear regression does assume this in that it tries to find the optimal linear combination of predictors to represent a dependent variable. However, there's nothing stopping a researcher from feeding in e.g. x and x squared as predictors, and thereby finding the best quadratic relationship between x and some dependent variable.