Sugar and Slugs

Every so often, I read an article quoting some claim arising from research on sex differences. Typically, scientists have found some sex difference that they have found to be statistically significant, and the difference is reported with much fanfare and various claims about what this difference means. Unfortunately, a statistically significant difference is not necessarily a useful difference in practice, making many of the ways people interpret the original narrow scientific claim simply wrong.

A naïve perspective on a statistical difference often goes like this: “I heard that girls aren’t as good at spacial rotation as boys are, so I guess that explains why I get lost so easily.”  Or, for other differences, someone might say, “Apparently, they’ve found that gay men’s index fingers are longer than straight men’s, so I measured mine and my partner’s. I passed the test, but my partner failed it—I guess he is a bit straight acting.” Or, “Researchers have found that transsexual brains are different from cissexual brains. If only they could have tested me when I was a child, I could have diagnosed back then and been saved a lot of pain.”

The problem here is a fundamental misunderstanding of what statistics tell us. Statistics tell us about properties of samples taken from populations. They don’t necessarily tell us about individuals. To understand why, we’ll look at three sex differences, height, finger length, and brains. Tasty, tasty brains.

Measuring Up the Sexes

Let’s start with height, an “obvious” sex difference. In most human populations, men are noticeably taller than women. For our discussion, we’ll use demographic data from the USA. Men have a median height of 5′ 8.5″ (174 cm), whereas women have a median height of 5′ 3.5″ (162 cm). Thus, we have a tangible sex difference of five inches between the sexes. (I used the median as my “average” here, rather than the mean—see Note 1 at the bottom.)

So, we have a statistic, and it meshes well with our daily observations of life, but what do these numbers actually tell us? Can we use height as a predictor for sex, and, if so, just how good a predictor is it?

To try to answer that question, let’s arbitrarily pick someone from the US population who is 5′ 6.5″ (169 cm) tall, making them 3 inches taller than the median for women, and two inches shorter than the median for men. Naïvely, you might expect that this individual is more likely to be a man than a woman; after all, the person’s height is closer to the male median than the female one. But you’d be wrong. In fact, statistically, it is slightly more likely that the person we have picked is a woman.

It isn’t enough to have a sense of the height of the average man or the average woman. We also need to know something about the distribution of heights in the population. If we measured one million Americans, chosen at random, we would expect to get statistics that look like the ones below:

Height (cm)	Women	Men	P(Woman)	P(Man)
75–80	146		100.00%
80–85	907	66	93.22%	6.78%
85–90	1,911	2,115	47.47%	52.53%
90–95	3,915	4,708	45.40%	54.60%
95–100	4,805	4,727	50.41%	49.59%
100–105	5,050	4,015	55.71%	44.29%
105–110	5,303	3,941	57.37%	42.63%
110–115	4,949	5,462	47.54%	52.46%
115–120	4,461	6,651	40.15%	59.85%
120–125	5,347	7,371	42.04%	57.96%
125–130	5,728	6,404	47.21%	52.79%
130–135	6,199	5,475	53.10%	46.90%
135–140	5,265	5,056	51.01%	48.99%
140–145	6,461	7,699	45.63%	54.37%
145–150	20,368	6,669	75.33%	24.67%
150–155	55,291	7,245	88.41%	11.59%
155–160	99,716	10,198	90.72%	9.28%
160–165	122,115	24,369	83.36%	16.64%
165–170	100,480	57,832	63.47%	36.53%
170–175	41,443	94,289	30.53%	69.47%
175–180	9,660	102,024	8.65%	91.35%
180–185	1,521	74,355	2.00%	98.00%
185–190	54	33,282	0.16%	99.84%
190–195		12,338		100.00%
195–200		2,212		100.00%
200–205		402		100.00%
Total	511,095	488,905	51.11%	48.89%

One of the interesting points to note from this data is that our sample has more women than men, because there are more women than men in the US population (men are more likely to die). But it is also the case that men occupy a greater range of heights, so they are necessarily spread more thinly.

If we plotted these counts, they would look like the following:

The graph shows two distributions, one for men and one for women (each roughly following the classic bell-curve shape of a statistical normal distribution). At a little before 5′ 7″ (169.5 cm), they cross—anyone taller than that appears to be statistically more likely to be a man, and anyone shorter is more likely to be a woman. It might seem that we could use this value as a threshold between “female” heights and “male” heights. But there are a lot of people “on the wrong side” of the cut off point. More than 1 in 9 women (11.6%) are taller than 169.5 cm (the shaded pink section of the graph), putting them on the side we might have described as “more likely to be male”, but there are even more men who have “girly” heights: almost exactly one third of men (33.4%) are shorter than 169.5 cm.

So, imagining that there is a height threshold that we could use to reliably partition women from men is false. And our daily experience backs that up. There is a lot of overlap between the range of heights for men and the range of heights for women. Statistically, it may be the case that someone who is 5′ 6″ (167 cm) is more likely to be a woman, but 1 in 3 people of that height are men, which means as a test to sort men and women, a height threshold would be wrong quite often. Of course, in places where the distributions have less overlap, things are more clear cut; for example, only 1 in 10 of people 5′ 3″ (159.5 cm) tall are men, and only 1 in 10 people 5′ 9.5″ (176.5 cm) are women. And there are very few women taller than 6′ 1″ (185 cm).

It’s tempting to imagine we might be able to salvage the simple threshold idea by saying that people taller than 176.5 cm are usually male and a those shorter than 159.5 cm are usually female, and abandoning everyone the middle (more than half of our sample!) as living in a gray area. But even that doesn’t work. Once we get to people shorter than 4′ 9″ (145 cm), there is no reliable gender difference. There are 60,447 women less than 145 cm tall, and 63,690 men, making it pretty much a wash. But because there are more men than women, there are proportionately more particularly short men (13% of all men) than short women (11.8% of all women).

So even though height is a sex difference that is fairly visible in the world, and the difference between the height of men and women is statistically significant, it’s difficult to put this information to good use to make predictions about individuals. If all we know is that men have heights centered around 5′ 8.5″ and women have heights centered 5′ 3.5″, that’s not enough information to make any reasonable predictions at all about individuals, and certainly not enough to use someone’s height to predict whether they are male or female. Even with a better sense of how height is distributed, we can only predict gender with at least 90% accuracy for 34.7% of people, mostly people who are really tall (but also people between 5′ 1″ and 5′ 3″). In other words, even though knowing the statistical distributions for the heights of men and women can tell us something some of the time, for most people, knowing their height is useless for making reliable predictions about their sex.

Furthermore, we have not even begun to consider other factors that influence height, such as race and ethnicity. For example, an average woman from Norway is 5′ 6.5″, but an average man from rural India is only 5′ 3.5″. So, any generalizations we make about height are “all other things being equal”, but in real life, those other factors are not being held constant.

And that was height—what about other sex differences? After all, you’re hardly likely to be considered to have made a major research discovery if you announce that on average men are taller than women.  Typically, research on sex differences focuses on more subtle, less obvious differences. These differences are good for headlines, but at least some of the time, the differences that are uncovered, while “statistically significant”, are even less practically significant than height when applied to individuals.

Pull My Finger

With height under our belt, let’s move on to looking at a more subtle sex difference, finger length, specifically the 2D:4D ratio. Here’s what Wikipedia says about it (at the time of writing):

The digit ratio is the ratio of the lengths of different digits or fingers typically measured from the bottom crease where the finger joins the hand to the tip of the finger. It has been suggested by some scientists that the ratio of two digits in particular, the 2nd (index finger) and 4th (ring finger), is affected by exposure to androgens e.g. testosterone while in the uterus and that this 2D:4D ratio can be considered a crude measure for prenatal androgen exposure, with lower 2D:4D ratios pointing to higher androgen exposure. The 2D:4D ratio is calculated by measuring the index finger of the right hand, then the ring finger, and dividing the former by the latter. A longer ring finger will result in a ratio of less than 1, a longer index finger will result in a ratio higher than 1.

The 2D:4D digit ratio is sexually dimorphic: in males, the second digit tends to be shorter than the fourth, and in females the second tends to be the same size or slightly longer than the fourth.

A number of studies have shown a correlation between the 2D:4D digit ratio and various physical and behavioral traits.

It seems to say, then, that we can set a threshold of one for the ratio, with men on one side and women on the other. If you have a ratio of less than one (longer ring finger), you have “boy fingers”, and if you have a ratio of greater than one (longer index finger), you have “girl fingers”. It’s a simple rule that’s easy to remember. But we ought to be suspicious. We had a threshold for height for (169.5 cm), but a full third of men fell into the “girly height” category. And we haven’t been told anything about the average ratios for men and women, or their distribution. At the time of writing, no such information is on Wikipedia about that.

So, let’s dive into a paper on the topic. For convenience I’m going to pick just one paper that has a moderately good sample size, The Visible Hand: Finger Ratio (2D:4D) and Competitive Behavior, by Matthew Pearson and Burkhard C. Schipper. Here’s their data (see Note 2):

Race	Sex	Count	Average	Std Dev	Min	Max
White	Male	35	0.960	0.026	0.899	1.022
Asian	Male	47	0.944	0.026	0.882	1.000
Hispanic	Male	10	0.954	0.025	0.913	1.002
Black	Male	2	0.951	0.015	0.941	0.962
Others	Male	6	0.973	0.025	0.938	0.998
All	Male	100	0.952	0.0272	0.882	1.033
White	Female	20	0.959	0.030	0.898	0.999
Asian	Female	65	0.963	0.026	0.912	1.033
Hispanic	Female	5	0.948	0.043	0.898	0.996
Black	Female	1	0.917		0.917	0.917
Others	Female	7	0.978	0.034	0.942	1.033
All	Female	98	0.962	0.0293	0.898	1.033

The first intriguing detail is that no group has an average greater than one. Women, in aggregate, do not match our intuition of a “girly“ finger length ratio at all, averaging 0.962. Also, while the paper does claim to have found a statistically significant difference between men and women in general, for white women, they failed to find any significant difference at all, which is just as well, because their white women had more mannish hands than their white male counterparts—oops!

The authors of the paper don’t give us the distribution for their data, but they do give the standard deviation, and so it is reasonable to assume that we can approximate it with a normal (bell-curve) distribution. The graph below shows what the two distributions look like:

As you can see, there is a lot of overlap. If we used finger length as a sex test, it would be right only 56.7% of the time. It’s only better than 75% accurate for people with finger length ratios of 1.02 and above, and only 1.6% of the population are fall into that category. It’s only 90% accurate for people with a finger ratio of 1.06, which is a tiny 0.026% of the population.

Also, while the test can accurately identify a very small number of women, it can never accurately identify men. It is at its most clear-cut at a ratio of 0.89; 3 out 5 people with that ratio (60%) are men.

So while the researchers for this paper did find an actual “statistically significant” difference between the finger ratios of men and women, in practice the difference is not one we can usefully apply to individuals.

Other researchers have examined finger-length ratios of smaller groups, including gays and lesbians and transsexuals. They, too, have found “statistically significant” differences, but we have no reason to expect that they will be any more useful, especially as the sample sizes for these groups are smaller and the differences observed more subtle, as we will see in our final sex difference.

Brains, Bring Me Brains

Brains are a favorite choice of sex-differences researchers, so let’s pick one random study of brains, namely Male-to-Female Transsexuals Have Female Neuron Numbers in a Limbic Nucleus, by Kruijver, et al. Here’s a summary of their results (see Note 3):

	Subjects	Mean BSTc	Std Dev
Cissexual Gay Men	9	34.6	10.20
Cissexual Straight Men	9	32.9	9.00
Transsexual Women	6	19.6	8.08
Cissexual Women	10	19.2	7.91

A naïve view of this data is that transsexual women’s brains look a lot like cissexual women’s brains, and unlike the brains of cissexual men. We might also naïvely suppose from these results that we could have a “brain femininity” test and use it to detect transsexual women (provided that we found killing them and dissecting their brains to make the measurement to be a good trade-off!).

You might be concerned that someone is making a generalization about the world’s hundreds of thousands of transsexuals (we can estimate at least 350,000 in the USA and Europe alone) from comparing six transsexual brains, but let’s forget the issue of tiny sample sizes for now. Instead, we’ll assume that the average and standard deviation values are accurate, and come from data with a normal distribution.

If we examine the distributions, their probabilities look like the following:

Here, we can see that despite the mean BSTc count of the women’s brains being almost half that of men, there is still considerable overlap. If we just compare cissexual men with cissexual women, and say that BSTc counts above 26.5 × 10³ are male and those below female, we find that that 23.9% of men have “female” brains, and 17.8% of women have “male” brains.

But the picture changes a lot when you look at the distributions in a context where everyone comes from the same population. As before, we’ll use an imagined sample of one million people from the general population of the USA. In that sample, we’d expect to see about 510,000 cissexual women, 444,000 straight cissexual men, 45,000 cissexual gay men, and a mere 500 transsexual women (plus 500 transsexual men). When we scale the curves proportionately, the bell curve for gay men (about 1 in 10 of the population) drops a good deal, but the curve for transsexual women (about 1 in 1000) flatlines. (I didn’t color the x-axis red; that’s the line for transsexual women.)

Now we can see why a test for transsexualism based on measuring BSTc is not possible (other than that annoying brain-dissection requirement). If we have someone who was born physiologically male who comes to us, survives a brain examination, and appears to have a very “girly” BSTc count in the 14 × 10³ range, there will be about 2175 straight cissexual men with a similar value, against only 19 transsexual women. In other words, if you used BSTc measurments to test for transsexualism, you’d only be right on someone with a brain this “girly” a mere 0.76% of the time. Even worse, fewer than 25% of transsexual women’s brains would score this (or more) “girly”—those with less girly brains are even tougher to correctly identify with our putative transsexuality test.

So, even if these researchers have found a statistically significant difference in the brains of transsexual women from their tiny sample of six women, in practice, it is of little use to individual transsexuals.

What Would a Useful Sex Difference Look Like?

If you’re hoping for a sex difference that might be helpful for some kind of test applied to individuals, let me give you some rules of thumb. For gay people (or any group that is about 1 in 10 of the population), you want a difference where there isn’t too much overlap between the two distributions. Since the larger the standard deviation, the greater the overlap, we can set a rule for the maximum standard deviation that will avoid exessive false positives. Find the distance between the average values for men and women (or whatever groups we’re distinguishing between), and divide it by 4. The standard deviation should be no larger than the result. For example, if men average 25 and women average 33, the groups are 8 apart and the maximum workable standard deviation is 2. If it is worse than that, you’ll have excessive false positives (more than about 25%) from opposite-sexed heterosexuals in the tail of their distribution.

For a sex-difference based test for transsexualism (or any group that is about 1 in 1000 of the population), the sex difference needs to have an even smaller standard deviation. To get the rough value you need, divide the distance between the two averages by 7 instead of 4. But the truth is, that would give you two distributions that barely touch at all. Very, very few sex differences are going to be that clear cut. For that reason, you can probably count on there never being a useful and reliable test for transsexualism based on sex differences.

A Test That Works

Despite all that I have said, there is a pretty accurate test for transsexualism, based on sex differences. Simply tell your would-be transsexual about this sex difference, and watch them. If you see them frantically measuring their fingers, or wishing they could scan their brains, they’re probably transsexual. It’s probably not that accurate, but it’s better than we’d do from actually scanning their brains or measuring their fingers.

Notes

1. I used the median for the height data because the average and standard deviations are skewed by the long tail at the left of the distribution. The tail is presumably caused by the various disorders that can cause stunted growth.
2. In their original paper, Pearson & Schipper seem to come up with a different total, and, more importantly, their standard deviation lacks precision. By using some tricks with the data they do give, I calculated a standard deviation with three digits of precision rather than two.
3. The paper presents SEM values (standard error of the mean), rather than standard deviation, but we can convert between the two using the formula stddev = SEM × sqrt(sampleSize).

Every once in a while, I get into a discussion and one of two ideas about “labels” comes up,

• That labels are bad, because each person is special in their own way, and so should not be constrained by some label.
• That people ought to be free to redefine the meaning of labels as they see fit.

Both of these ideas are wrongheaded. Labels exist to facilitate communication; they allow us to fill in a lot of blanks with defaults that can still be overridden by other things we learn about the person. A label does not define someone—it merely provides a useful first-order approximation about some aspect of them.

If someone tells you that I am a liberal/progressive, it tells you a lot about me. It means that I probably won’t be picketing abortion clinics, voting against gay marriage, or demanding tax breaks for billionaires. But it won’t tell you whether I’m an atheist, a liberal christian, or someone who believes in the healing power of crystals—we can easily imagine any of those options. It might make my being a vegetarian a little more likely, but it doesn’t require that I not eat meat.

If a label doesn’t adequately describe me, the right option for me is to either not use it at all, or just add appropriate modifiers to it. For example, I often describe myself as “someone in a same-sex relationship” rather than as “a lesbian”. I think it fits me better. But I don’t mind if other people describe me as a lesbian, and will use it myself on occasion. I’m enough of a lesbian that the correlations outweigh the things that don’t fit (in particular, “lesbian” implies that I am attracted to women in general, rather than in love with one particular woman).

If a label doesn’t fit me perfectly, it certainly doesn’t mean that I need to change myself to fit a stereotype that matches the label. It just means that a bit more explaining is in order before someone knows what my deal is. But that is still less explaining than we’d have to do if we tried to have no labels at all. And it certainly doesn’t mean that I should try to redefine the label so that it either fits me better or so that I can reject it entirely. So, saying, “I’m not a lesbian because I don’t cruise lesbian bars”, or, “I’m not a lesbian because I’m not a Wiccan”, is foolish.

For labels (or any linguistic communication) to work, there has to be a rough consensus about what words mean. Over time, as a culture, we come to interpret words in particular ways, and it is the commonality of our understanding of what words mean that makes communication possible. The meanings can change, and new meanings can be added (so “gay” can go from “happy” to “attracted to the same sex”), but the stability of meaning over reasonable timeframes is what allows us to communicate meanings quickly. If I say that I’ve been crying and need a hug, it’s so much better if you don’t have to ask me what I mean by “crying” and what constitutes a “hug”.

Obviously, the meanings of words aren’t static, and so we’re all free to try to stretch existing words to fit new contexts or develop neologisms to where existing words fail. For example, “heterosexual” and “cissexual” are relatively new terms and both have proved useful alternatives to terms like “normal” that people might have used previously. But coining new words and changing the meanings of existing ones isn’t easy and often fails to get any traction—quite often rightly so.

If a male friend of your comes up to you and says, “I need to tell you, I’m gay”, and then it turns out that what he really meant was that he was happy and didn’t have a care in the world, you might very well tell him that he can take his attempt to redefine “gay” (reclaiming its old meaning) and shove it.

Likewise, if your friend says, “I saw a transhumanist yesterday”, and it turns out that he was actually talking about someone who changed their sex, it’s reasonable to point out that he’s a bit confused about terms. Sometimes people dig in when you do this, and he might argue that people who change their sex are transhumanists. But even if he can somehow make a tenuous argument that this is true, from the perspective of successful communication, he’s wrong—he has used the wrong word and that’s that.

Finally, because labels are part of consensus communication, we don’t get to completely control them. It’s one thing if the label doesn’t match up (e.g., calling me a “religious conservative” would be pretty ludicrous), but if the only problem I have with the label is that it oversimplifies who I am (e.g., that calling me a lesbian doesn’t capture every nuance about my sexuality), maybe the right thing for me to do is get over myself and realize that sometimes other people don’t have the time or inclination to write many words where one or two are sufficient for their needs.

It’s true that when it comes to how people understand me, there are some aspects I think should be more salient than others, and so if I’m introduced to someone new, the order of labels probably does matter, and usually there are many many labels that are frankly irrelevant and don’t come up at all. But these issues are independent of labels themselves, the same issues would apply if we were trying somehow to avoid labels entirely—arguably, the added long-windedness would make the situation worse.

In short, a label does not define me, it describes me in a useful, yet incomplete way. And people who think otherwise, well, I have a label for them…

This post is, in some sense, a counterpoint to my Binaries Aren’t Intrinsically Bad post because part of its message is that some binaries are intrinsically bad, but arguably it’s not a counterpoint at all, since those binaries are really two sides of related but different stories.

A false dichotomy is a situation where you lead people to believe that they have to see things in one of two ways, and that those are the only choices.  George Bush’s claim that “You’re either with us, or with the terrorists!” is one such example, where any middle ground is missing.  But it’s worth realizing that sometimes there isn’t a continuum that connects the two extremes that form the dichotomy. If I say, “Which is it, do you like cats, or are you a communist?”, the problem isn’t that I’ve left you with no middle ground, it’s that the idea of any sort of sensible “middle” between these two options is fundamentally flawed, because these two concepts are orthogonal—each can be true or false independent of the other.

When it comes to identity, whether it is sexual orientation or gender identity, some people feel compelled to wonder, “Are you born that way, or is it a choice?”  Most people seem to accept that as a valid question to ask, but I would argue that it’s not—it’s a false dichotomy, and one of the second kind where there isn’t even a middle ground: these are two unrelated propositions, and, worse, the true underlying question, whether we can (or should) change who we are to avoid perturbing other people, is barely addressed at all. To understand why, let’s first ask the same question about some tamer topics and see how it holds up there.

Suppose that at some point growing up I had a bad experience with a particular food, let’s say peppermint liqueur, and now even just the smell of it turns my stomach—to actually drink it would make me gag. It would be absurd to imagine that I have made a conscious choice to gag on peppermint liqueur.  Who on earth would want that experience for themselves, with all of its potential for awkward social situations? You’d be a crazy masochist to want that.  So it seems reasonable for me to say, “I didn’t choose to be a peppermint liqueur gagger”, but it would be absurd to imagine that I was born to gag on peppermint liqueur (and pretty crazy to go searching for a gene to blame it on).

But just because something wasn’t preordained at birth doesn’t mean it is a thing that I can easily change. Maybe I can hide it a little, but whatever causes me to react in this way seems to be locked-in somehow, outside the reach of my will (and, for the sake of our thought experiment, we’ll suppose that no presently known therapy can change it either, even though that might not be true for some food aversions).  Thus, I have a trait that I didn’t actively choose, and I can’t seem to change, even though it isn’t a trait I was born with.

So, “I didn’t choose it and I can’t seem to change it” doesn’t necessarily imply “I was born that way”.  What about the other direction, does “I was born that way” imply “I can’t change it”? I would say obviously not—let’s do a simple example for that one…

There are some people who are born with imperfect looking teeth.  Sure, they were born that way, but their biology is not their destiny.  Orthodontists will happily straighten those teeth for a fee and they’ll be on their way, dental predestiny averted.

The fact that our biology does not always predetermine our fate does not mean that we can always override the cards that biology deals us, or that on those occasions when we can override it, that doing so is necessarily easy enough to be worth our effort.  Inborn or acquired, a trait we have may prove to be beyond our capacity to change by effort of will alone.

But the fact that we can’t change a trait we have at will does not mean that the traits that make up our identities are locked into a fixed, immutable state.

It seems reasonable to assume that at least some of the traits that make up our identities are driven by slow-moving, tectonic forces (themselves driven by deep undercurrents), manifesting as subtle gradual movement in a particular direction, with, of course, the potential for an occasional earthshaking jolt.  We may be no more able to control these aspects of ourselves than the populations of Australia and Hawaii can stop their land masses from drifting towards each other.

The LGBT community, with its embrace of the “born that way” side of the false “born that way or chose it” dichotomy often seems unsure of how to rationalize the experience of women who embrace lesbian sexuality later in life.  To avoid the idea that lesbian women could just choose to turn straight again, they are forced to try to fit the experience of these women into the “born that way” narrative by retconning it, rather than admitting that someone’s identity can evolve, unbidden.

Despite its obvious flaws, the “born that way” narrative remains ascendent. I think the idea of biological predestination lets people feel better.  They can say “I was always going to be gay/trans/right handed/Mormon/an asshole/an introvert/whatever, so there’s nothing I could have done.”  It’s one of those nice, clean, pat answers people like. Sure, it reduces a complex phenomenon to an easy answer; sure, it’s incoherent; and, as we have seen, the second half doesn’t even necessarily follow from the first; but in my experience, most people generally don’t require consistency or logical correctness from the things they believe; they’re looking for comfort and validation.  In a hostile world, they want to be absolved and be told that their present situation is not their fault.

(Actually, that’s oddly dualistic view of things; to say that you and your biology are two separate entities, and that we can lay blame with one rather than the other.  It’s almost like a murder trial where the killer asks to be absolved because it wasn’t he who committed the crime, it was his hand.)

In my model, I say that my identity is what it is because of an unfathomably complex tangle of innate proclivities, acquired traits, external forces, and a healthy dollop of random chance, stirred gently by a poorly understood feedback loop. That explanation is probably less satisfying for some, but for me, it makes more sense, and I like the idea that I am, at least in part, responsible for making me the person that I am, even if I don’t always understand how I did it.

With my understanding, it is at least conceivable that had I made different choices, I might have become a quite different person.   This view is pretty heretical in gay and trans communities, because it implies that there could be courses of action you might take, or have taken, that might affect your chances of ending up gay or trans.

I agree that this explanation might be more politically awkward, but if that explanation turns out to be the true one, truth should really win out over political expediency.  And, really, so what?

This dichotomy exists largely because of people of a conservative/religious persuasion who want to vilify “otherness”. Their mantra is “those people are different from us, but they could chose to conform if they wanted”.  You don’t have to say you’re at the mercy of biological predestiny to refute that—it’s sufficient to say that you’re unable to change your identity by any effort of will.  But frankly, why should I even be willing to try to change my identity by an effort of will to placate these douchebags?  The real response to people who vilify otherness and desire conformity is, “Screw you! It takes all sorts to make a world.”