From e7ce9d69dd3a211621926530811810b121c3c433 Mon Sep 17 00:00:00 2001 From: "M. Taylor Saotome-Westlake" Date: Sun, 16 Feb 2020 18:44:56 -0800 Subject: [PATCH] Human Diversity review Sunday night sprint session 2 --- content/drafts/book-review-human-diversity.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/content/drafts/book-review-human-diversity.md b/content/drafts/book-review-human-diversity.md index 9964c9d..640039c 100644 --- a/content/drafts/book-review-human-diversity.md +++ b/content/drafts/book-review-human-diversity.md @@ -14,7 +14,7 @@ The second part of this blog post is irrelevant. _Human Diversity_ is divided into three parts corresponding to the topics in the subtitle! (Plus another part if you want some wrapping-up commentary from Murray.) So the first part is about things we know about some ways in which female people and male people are different from each other! -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 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. +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. [multivariate effect sizes and the Marco del Guidice fan club, https://www.lesswrong.com/posts/cu7YY7WdgJBs3DpmJ/the-univariate-fallacy ] @@ -30,9 +30,11 @@ The second part of the book is about some ways in which people with different an 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 "pure" cluster. And so on. -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 regularities in the dataset (which is _too large_ for humans to hold in their heads all at once): a map that reflects a territory. +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. +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 2s of sweeping to fixation), but a lot of hypotheses about recent human evolution weren't easy to test, or formulate, until the genome was sequenced! +our migration out of Africa Humans interbred with Neanderthals and Denisovans -- 2.17.1