Rain comments on Even if you have a nail, not all hammers are the same - Less Wrong

95 Post author: PhilGoetz 29 March 2010 06:09PM

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Comment author: PhilGoetz 30 March 2010 03:57:55PM *  6 points [-]

Not willing to take the time to understand the math, ... Without taking classes in statistics and/or medicine, how can I become less wrong on problems like this?

You can't learn to be less wrong about mathematical questions without learning more math. (By definition.)

Comment author: Rain 30 March 2010 04:27:19PM *  1 point [-]

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.

Comment author: PhilGoetz 30 March 2010 06:39:04PM *  14 points [-]

The general advice here is

  • Not all regression is the same; beware anyone who reports doing "a regression"
  • Linear regression assumes a linear relationship
  • Don't trust a report that bases its authority on numbers if you can't say what those numbers mean
  • A conclusion can be both true and misleading
  • A little unreflective folk-psychology ("vitamins" as being "more is better" instead of having a dose-response curve) can do a lot of damage
Comment author: cupholder 30 March 2010 07:26:22PM 7 points [-]

All of those points are true, but there's one I'd like to flag as true but potentially misleading:

Linear regression assumes a linear relationship

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.

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