More Common Myths Debunked

Tansmisic claim: “Analysis of Ministry of Justice (MoJ) data from 2019/2020 indicated that approx 59% of trans women in prison had at least one conviction for a sexual offence against 17% of other men in prison.”

or something similar. This is statistical sleight of hand, here’s why:

The numbers behind those percentages:

• Trans women prisoners: 129 total → 76 are sex offenders = 58.9%
• Cis men prisoners: ~78,781 total → ~13,234 are sex offenders = 16.8%

What this actually shows:

• 76 trans women sex offenders in prison
• ~13,234 cis men sex offenders in prison

The percentage comparison hides the real disparity: You’re comparing 76 to 13,234—a completely different scale.

For this reason, things like per capita and ratio/percentage comparisons, are often prone to over representation and are therefore, misleading. And here we must employ some nuance and understanding, because there is no denying an over representation; however, the point is that when the numbers are so disparate, the over representation doesn’t matter. It may be statistically significant, but it is practically irrelevant. For example:

Imagine two classrooms:

• Classroom A: 129 students total, 76 like chocolate = 58.9%
• Classroom B: 78,781 students total, 13,234 like chocolate = 16.8%

If you say “Classroom A likes chocolate more!” you’d be wrong. Classroom B has way more chocolate lovers in absolute numbers; it just looks smaller as a percentage because the classroom is gigantic.

That’s exactly what the 59% vs 17% comparison does. It hides the massive difference in group sizes.

But what about per-capita?

Why Per-Capita Distorts the Picture

Much of the time, someone thinks they are clever and will say “it’s like you don’t understand per-capita.” This is a different sleight of hand ploy, but related because it still depends on a large disparity between populations (denominators, here).
The problem:

• Trans women population: 48,000 (tiny denominator)
• Cis men population: 29.2 million (huge denominator)

With such different population sizes:

• A change of just 6 convictions flips the trans women rate dramatically
• 76 convictions ÷ 48,000 = 15.83 per 10,000
• 82 convictions ÷ 48,000 = 17.08 per 10,000 (a 7% change in rate from just 6 more cases)

But for cis men:

• You’d need hundreds of extra convictions to move the needle meaningfully
• 13,234 ÷ 29,177,200 = 4.54 per 10,000
• You’d need ~13,400+ convictions to get to 4.6 per 10,000

Yet from this comparison, it looks like trans women will offend 3.5 times more (or there abouts)! That sounds really bad!

But it is not, because that 3.5 times more is concerning a fraction of a fraction of a percent of a larger population. It concerns 76 trans women out of 48k trans women, which is a fraction of .1% of the UK population.

Some will argue ‘but per-capita shows over representation!’ That’s true; but when comparing populations that differ by a lot (600x ish), small absolute changes produce large percentage swings. Like those six extra convictions we added? It disproportionately inflated the trans women numbers vs the cis men.

And remember, the policy question isn’t ‘which group’s percentage is higher?’ It’s ‘who commits these crimes?’ Answer: 99.43% cis men, 0.57% trans women.

When per capita is abused, which it absolutely has been, as history teaches us, then over policing of minority groups occurs while the majority of the people actually doing the crime are ignored, as they believe the data shows them where the real trouble spots/groups are.

One could probably argue that the per capita rate does not show over representation of a group for committing the crimes, but are convicted for the crimes. These are two different things - but I am not getting into that tangent here - that is a different issue.

The Math

Trans women population: 48,000
Trans women as % of UK population: 0.1%
Sex offense trans women in prison: 76

76 out of 48,000 trans women = 0.158% of trans women population

Now, what fraction of the TOTAL UK population is this?

UK population: ~59.6 million

76 ÷ 59,600,000 = 0.0000012755 = 0.000128%

Or: 1 in 784,000 people in the UK

So you see, once you put it into the right context, it turns out to be not more statistically significant than noise.

Great! So what IS a fair comparison?

Glad you asked!

Population comparison is perfectly fine, when you are talking about a population as a whole! So, to stay in the same frame of reference as our trasmisic pal, we want to compare trans women to cis men, in as much as who has the greater proportion of sex offences as a part of their conviction.

First we total up all those with sex offenses: 3,234 + 76 = 13,310

NOW we find our ratios and can put it into a nice chart, like so:

Group | Total Sex Offenders | Percentage of All Sex Offenders|
Cisgender Men | 13234| 99.43%|
Trans Women | 76 | 0.57%|
Total | 13310| 100.00%|

And now when we want to ask the question “who is responsible for the most sex offenses based on convictions in prison?” we can look at the absolute numbers, see the ratio of group A and group B to the category in question, and determine which one you have more of, and which one is a greater risk to you (or not).

If we took the per capita rate, or the original 59% vs 17% claim and comparison, we could end up focusing a lot of resources and police on trans women in an attempt to combat sex offenses in general (which is a very broad category), when they are the smallest population of offenders with the least compared to the population as a whole.

Yes, I intentionally left out cisgender women - I have very little reliable data to make this comparison here

Also, there is that infographic that goes around show per capita rates - this argument rebuttal here works for that as well, though that uses different numbers and data. I will eventually get around to making a section for that infographic as well. It has been well covered and debunked already, but in essence, they are using per capita sleight of hand. If they used absolute numbers, and percentages, categorized appropriately, it would not be to their advantage.

Remember: “There are lies, damned lies, and statistics”