The curmudgeonly view epitomized by Turkheimer says that science is about understanding the _causal structure_ of phenomena, and that polygenic scores don't fucking tell us anything. [Divorce is heritable _in the same way_ that intelligence is heritable](http://www.geneticshumanagency.org/gha/the-ubiquity-problem-for-group-differences-in-behavior/), not because there are "divorce genes" in any meaningful biological sense, but because of a "universal, nonspecific genetic pull on everything."
-Notably, Plomin and Turkheimer aren't actually disagreeing here: it's a difference in emphasis rather than facts. Polygenic scores _don't_ explain mechanisms—but might they end up being useful, and used, anyway? Murray's vision of social science is content to make predictions and "explain variance" while remaining ignorant of ultimate causality. Meanwhile, my cursory understanding (while kicking myself for [_still_](/2018/Dec/untitled-metablogging-26-december-2018/#daphne-koller-and-the-methods) not having put in the hours to get much farther into [_Daphne Koller and the Methods of Rationality_](https://mitpress.mit.edu/books/probabilistic-graphical-models)) was that you need to understand causality in order to predict what interventions will have what effects—maybe our feeble state of knowledge is _why_ we don't know how to find reliable large-effect environmental interventions that still yet might exist in the vastness of the space of possible interventions.
+Notably, Plomin and Turkheimer aren't actually disagreeing here: it's a difference in emphasis rather than facts. Polygenic scores _don't_ explain mechanisms—but might they end up being useful, and used, anyway? Murray's vision of social science is content to make predictions and "explain variance" while remaining ignorant of ultimate causality. Meanwhile, my cursory understanding (while kicking myself for [_still_](/2018/Dec/untitled-metablogging-26-december-2018/#daphne-koller-and-the-methods) not having put in the hours to get much farther into [_Probabilistic Graphical Models: Principles and Techniques_](https://mitpress.mit.edu/books/probabilistic-graphical-models)) was that you need to understand causality in order to predict what interventions will have what effects—maybe our feeble state of knowledge is _why_ we don't know how to find reliable large-effect environmental interventions that still yet might exist in the vastness of the space of possible interventions.
There are also some appendicies at the back of the book! Appendix 1 (reproduced from, um, one of Murray's earlier books with a coauthor) explains some basic statistics concepts. Appendix 2 ("Sexual Dimorphism in Humans") goes over the prevalence of intersex conditions and gays, and then—so much for this post broadening the [topic scope of this blog](/tag/two-type-taxonomy/)—transgender typology! Murray presents the Blanchard–Bailey–Lawrence–Littman view as fact, which I think is basically _correct_, but a more comprehensive treatment (which I concede may be too much too hope for from a mere Appendix) would have at least _mentioned_ alternative views ([Serano](https://rationalwiki.org/wiki/Intrinsic_Inclinations_Model)? [Veale](/papers/veale-lomax-clarke-identity_defense_model.pdf)?), if only to explain _why_ they're worth dismissing. (Contrast to the eight pages in the main text explaining why "But, but, epigenetics!" is worth dismissing.) Then Appendix 3 ("Sex Differences in Brain Volumes and Variance") has tables of brain-size data, and an explanation of the greater-male-variance hypothesis. Cool!
It's important not to overinterpret the IQ-scores-by-race results; there are a bunch of standard caveats that go here that everyone's treatment of the topic needs to include. Again, just because variance in a trait is statistically associated with variance in genes _within_ a population, does _not_ mean that differences in that trait _between_ populations are _caused_ by genes: [remember the illustrations about](#heritability-caveats) sun-deprived plants and internet-deprived red-haired children. Group differences in observed tested IQs are entirely compatible with the blank slate doctrine, a world in which those differences are entirely due to the environment imposed by an overtly or structurally racist society. Maybe the tests are culturally biased. Maybe people with higher socioeconomic status get more opportunities to develop their intellect, and racism impedes socio-economic mobility. And so on.
-The problem is, a lot of the blank-slate-compatible hypotheses for group IQ differences become less compelling when you look into the details. "Maybe the tests are biased", for example, isn't an insurmountable defeater to the entire endeavor of psychometrics—it's _itself_ a testable hypothesis, or can become one if you specify what you mean by "bias" in detail. If a test question were biased against a group, you would expect
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+The problem is, a lot of the blank-slate-compatible hypotheses for group IQ differences become less compelling when you look into the details. "Maybe the tests are biased", for example, isn't an insurmountable defeater to the entire endeavor of psychometrics—it is _itself_ a falsifiable hypothesis, or can become one if you specify what you mean by "bias" in detail. If a test question were biased against a group, you would expect
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