+The first (short) chapter is mostly about explaining [Cohen's _d_](https://en.wikiversity.org/wiki/Cohen%27s_d) [effect sizes](https://en.wikipedia.org/wiki/Effect_size), which I think are solving a very important problem! When people say "Men are taller than women" you know they don't mean _all_ men are taller than _all_ women (because you know that they know that that's obviously not true), but that just raises the question of what they _do_ mean. Saying they mean it "generally", "on average", or "statistically" doesn't really solve the problem, because that covers everything between-but-not-including "No difference" to "Yes, literally all women and all men". Cohen's _d_ is the summary statistic that lets us _quantify_ statistical differences in standardized form: once you can [visualize the overlapping distributions](https://rpsychologist.com/d3/cohend/), whether the reality of the data should be summarized in English words as a "large difference" or a "small difference" becomes a _much less interesting_ question.
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+Murray also addresses the issue of aggregating effect sizes—something [I've been meaning to get around to blogging about](/2018/Dec/untitled-metablogging-26-december-2018/#high-dimensional-social-science-and-the-conjunction-of-small-effect-sizes) more exhaustively for a while in this context of group differences (although at least, um, my favorite author on _Less Wrong_ [covered it in the purely abstract setting](https://www.lesswrong.com/posts/cu7YY7WdgJBs3DpmJ/the-univariate-fallacy)): small effect sizes in any single measurement can amount to a _big_ difference when you're considering many measurements at once. That's how people can distinguish female and male faces at 96% accuracy, even though there's no single measurement (like "eye width" or "nose height") offers that much predictive power.
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+[TODO: more examples of sex difference effect sizes, elaborate on "big" doesn't mean anything]
+
+Subsequent chapers address sex differences in personality, cognition, interests, and the brain. It turns out that women are more warm, empathetic, æsthetically discerning, and cooperative than men are! They're also more into the Conventional, Artistic, and Social dimensions of the [Holland occupational-interests model](https://en.wikipedia.org/wiki/Holland_Codes).
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+You might think that this is all due to socialization, but then it's hard to explain why the same differences show up in different cultures—and why (counterintuitively) the differences seem _larger_ in richer, more feminist countries. You might think that the "larger differences in rich countries" result is an artifact: maybe people in less-feminist countries implicitly make within-sex comparisons when answering personality questions (_e.g._, "I'm aggressive _for a woman_") whereas people in more-feminist countries use a less sexist standard of comparison, construing ratings as compared to people-in-general. Murray points out that this explanation still posits the existence of large sex differences in rich countries (while explaining away the unexpected cross-cultural difference-in-differences). Another possibility is that wealth increases sexual dimorphism _in general_, including, _e.g._, height and blood pressure, not just in personality.
+
+[TODO: tie into farmer/forager theory]
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+Women are better at verbal ability and social cognition, whereas men are better at visuospatial skills. The sexes achieve similar levels of overall performance via somewhat different mental "toolkits." Murray devotes a section to a 2007 result of Johnson and Bouchard, who report that ["_g_ masks the dimensions on which [sex differences in mental abilities] lie"](/papers/johnson-bouchard-sex_differences_in_mental_abilities_g_masks_the_dimensions.pdf): overall levels of mental well-functioning lead to underestimates of the effect sizes of specific mental abilities, which you want to statistically correct for. This result in particular is _super gratifying_ to me personally, because [I independently had a similar idea](/2019/Sep/does-general-intelligence-deflate-standardized-effect-sizes-of-cognitive-sex-differences/)—it's _super validating_ as an amateur to find that the pros have been thinking along the same track!
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+The second part of the book is about some ways in which people with different ancestries are different from each other! Obviously, there are no "distinct" "races" (that would be dumb), but it turns out (as found by endeavors such as [Li _et al._ 2008](/papers/li_et_al-worldwide_human_relationships_inferred.pdf)) that when you throw clustering and [dimensionality-reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction) algorithms at SNP data (<em>s</em>ingle <em>n</em>ucleotide <em>p</em>olymorphisms, places in the genome where more than one allele has non-negligible frequency), you get groupings that are a pretty good match to classical or self-identified "races".
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+Ask the computer to assume that an individual's ancestry came from _K_ fictive ancestral populations where _K_ := 2, and it'll infer that sub-Saharan Africans are descended entirely from one, East Asians and some native Americans are descended entirely from the other, and everyone else is an admixture. But if you set _K_ := 3, populations from Europe and the near East (which were construed as admixtures in the _K_ := 2 model) split off as a new inferred population cluster. And so on.
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+These ancestry groupings _are_ a "construct" in the sense that the groupings aren't "ordained by God"—the algorithm can find _K_ groupings for your choice of _K_—but _where_ it [draws those category boundaries](https://www.lesswrong.com/posts/esRZaPXSHgWzyB2NL/where-to-draw-the-boundaries) is a function of the data. The construct is doing _cognitive work_, concisely summarizing statistical regularities in the dataset (which is _too large_ for humans to hold in their heads all at once): a map that reflects a territory.
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+Twentieth-century theorists like Fisher and Haldane and whatshisface-the-guinea-pig-guy had already figured out a lot about how evolution works (stuff like, a mutation that confers a fitness advantage of _s_ has a probability of about 2<em>s</em> of sweeping to fixation), but a lot of hypotheses about recent human evolution weren't easy to test or even formulate until the genome was sequenced!
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+You might think that there wasn't enough _time_ in the 2–5k generations since we came forth out of Africa for much human evolution to take place: a new mutation needs to confer an unusually large benefit to sweep to fixation that fast. But what if you didn't actually need any new mutations? Natural selection on polygenic traits can also act on "standing variation": variation _already_ present in the population that was mostly neutral in previous environments, but is fitness-relevant to new selection pressures. The rapid response to selective breeding observed in domesticated plants and animals mostly doesn't depend on new mutations.
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+Another mechanism of recent human evolution is _introgression_: early humans interbred with our Neanderthal and Denisovan "cousins", giving our lineage the chance to "steal" all their good alleles! In contrast to new mutations, which usually die out even when they're beneficial (that 2<em>s</em> rule again), alleles "flowing" from another population keep getting reintroduced, giving them more chances to sweep!
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+Population differences are important when working with genome-wide association studies, because a model "trained on" one population won't perform as well against the "test set" of a different population. Suppose you do a big study and find a bunch of SNPs that correlate with a trait, like schizophrenia or liking opera. The frequencies of those SNPs for two populations from the same continent (like Japanese and Chinese) will hugely correlate (Pearson's _r_ ≈ 0.97), but for more genetically-distant populations from different continents, the correlation will still be big but not huge (like _r_ ≈ 0.8 or whatever).
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+What do these differences in SNP frequencies mean in practice?? We ... don't know yet. At least some population differences are fairly well-understood: I'd tell you about sickle-cell and lactase persistence, except [then I would have to scream](/2017/Dec/interlude-xi/). There are some cases where we see populations independently evolve different adaptations that solve the same problem: people living on the plateaus of Tibet and Peru have both adapted to high altitudes, but the Tibetans did it by breathing faster and the Peruvians did it with more hemoglobin!