-I agreed that for most people in most everyday situations it probably didn't matter. _I_ cared because I was a computational philosophy of gender nerd, I said, [linking to a program I had written](https://github.com/zackmdavis/Persongen/blob/8fc03d3173/src/main.rs) to simulate sex classification based on personality, using data from [a paper about sex differences in the "facets" underlying the Big Five personality traits](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3149680/). Sophia was impressed, but had some cutting methodological critiques. The paper had given the residuals of each facet against the other, so I assumed you could sample one, and then use the residual stats to get a "diff" from one to the other. Sophia pointed out that you can't actually use residuals for sampling like that, because the actual distribution of the residual was highly dependent on the first facet. Given an unusually high value for one facet, taking the overall residual stats as independent would imply that the other facet was equally likely to be higher or lower, which was absurd.
+I agreed that for most people in most everyday situations it probably didn't matter. _I_ cared because I was a computational philosophy of gender nerd, I said, [linking to a program I had written](https://github.com/zackmdavis/Persongen/blob/8fc03d3173/src/main.rs) to simulate sex classification based on personality, using data from [a paper by Weisberg _et al._ about sex differences in the correlated "facets" underlying the Big Five personality traits](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3149680/). (For example, studies had shown that women and men didn't differ in Big Five Extraversion, but if you split "Extraversion" into "Enthusiasm" and "Assertiveness", there were small sex differences pointing in opposite directions, with men being more assertive.) My program generated random examples of women's and men's personality stats according to Weisberg _et al._'s data, and then tried to classify the "actual" sex of each example given only the personality stats—only reaching 63% accuracy, which was good news for androgyny fans like me.
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+Sophia was impressed, but had some cutting methodological critiques. The paper had given [residual](https://en.wikipedia.org/wiki/Errors_and_residuals) statistics of each facet against the other—like the mean and standard deviation of Enthusiasm _minus_ Assertiveness—so I assumed you could randomly generate one facet, and then use the residual stats to get a "diff" from one to the other. Sophia pointed out that you can't actually use residuals for sampling like that, because the actual distribution of the residual was highly dependent on the first facet. Given an unusually high value for one facet, taking the overall residual stats as independent would imply that the other facet was equally likely to be higher or lower, which was absurd. Sophia built her own model in Excel using the correlation matrix from the paper, and found a classifier with 68% accuracy.