I was rereading this discussion of the "g factor" from "Three-toed sloth" and found a link to this excellent paper by "Glymour".
He spends most of the paper criticizing the statistical methods (multiple regression and factor analysis) used in the "Bell Curve". The later has always seemed a little odd to me with it's "latent variables" but it is (or at least was) the foundation of psychometrics and is used extensively in the real world to construct questionnaires. For instance, all the anti-depressants on the market today were approved based on a scale (usually the HAM-D) that was developed with factor analysis. Multiple regression applied to non-randomized experiments is the "back bone" of all social science statistics (e.g., economics, finance, sociology) and a lot of "biological" science (e.g., ecology and epidemiology).
Of both methods Glymour concludes:
"Hernstein and Murray use the tools of their profession, and social science
statistics generally, gave them. The tools were incompetent for the use
Hernstein and Murray put them to, but what were they to do?"
This statement will not surprise anyone who has been taught or has practiced the perils unscrambling the eggs of observational (e.g., non-randomized) data with linear or generalized linear models. But if we can ignore Hernstein and Murray because they use flawed methods who else can I ignore: the federal reserve, the USDA, the CDC, the guys who came up with my FICO score? Is there a crack in the inferential foundations of society? Are the inferers of my society on crack? Are we being led by court astrologers like Moldbug says?
That would be hilarious. Scary, but hilarious.
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