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naraburns  nihil supernum  1yr ago (text post)   35449 thread views

Culture War Roundup for the week of October 3, 2022

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While I grant that I may be falling for the "passing tranny fallacy"

I keep pointing out when people mention this: You can see a distribution, and notice that the distribution gets sparser when you get towards "better at passing, but where I can still notice them". You can then deduce that there aren't many who are so good that you won't notice them at all.

Even if we assume a uniform distribution, wouldn't it still be the case that the distribution of those you notice would still get sparser when you get towards "better at passing, but where I can still notice them?"

Like, imagine a toy model where there are 300 total MTF trans people, with 100 who are bad at passing, 100 who are better, and 100 who pass perfectly. You'll always notice the 1st 100, you'll notice the middle 100 about 50% of the time, and you'll never notice the last 100. By your observations, then, you'll notice 100 poorly-passing MTF and 50 better-passing MTF. Since the middle group is better-passing, you'll also notice that those 50 are qualitatively different - i.e. noticeably closer to actually passing - from the 100 in the 1st group, besides just noticing them only 50% of the time. But it would be wrong to conclude from this that then it follows that there are even fewer people in the 3rd group.

That's true if "better at passing" means "more often doesn't get noticed", but it's not true if "better at passing" means "still noticed, but doesn't look quite as bad". I was suggesting the latter scenario.

OK, I had to think about this for a while, but I think I follow. You're really only looking at those who are so far away from passing that they would fail to pass 100 times out of 100, and looking at the distribution of how badly they fail at passing? I think that makes sense, and you're probably correct in your inference.

It is probably a good heuristic but you are assuming the shape of the distribution and still then it could go wrong. For example let's say that there is a latent variable that measures the degree of femaleness that was uniformly distributed over trans and that your likelihood of clocking them decreases monotonically towards high femaleness then, depending on your clocking function, you could get the same picture of a sparser distribution towards high femaleness without it being so.

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