Comment by nonbel
6 years ago
>"The fact that these variables are all typically linear or additive further implies that interactions between variables will be typically rare or small or both (implying that most such hits will be false positives, as interactions are far harder to detect than main effects)."
Where does this "fact" come from? And if everything is correlated with everything else all these effects are true positives...
Also, another ridiculous aspect of this is that when data becomes cheap the researchers just make the threshold stricter so it doesn't become too easy. They are (collectively) choosing what is "significant" or not and then acting like "significant" = real and "non-significant" = 0.
Finally, I didn't read through the whole thing. Does he claim to have found an exception to this rule at any point?
> Finally, I didn't read through the whole thing. Does he claim to have found an exception to this rule at any point?
Oakes 1975 points out that explicit randomized experiments, which test a useless intervention such as school reform, can be exceptions. (Oakes might not be quite right here, since surely even useless interventions have some non-zero effect, if only by wasting peoples' time & effort, but you might say that the 'crud factor' is vastly smaller in randomized experiments than in correlational data, which is a point worth noting.)
Thanks,
How about this "fact": The fact that these variables are all typically linear or additive?
That is simply a corollary of the fact that Pearson's r and regressions are usually linear/additive, and things like Meehl's demonstration wouldn't work if they weren't. You'd just calculate all the pairwise correlations and get nothing if they were solely totally nonlinear/interactions. (In which case you'd have a hard time proving they were related at all.)
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