The "Trans Women Offend Like Men" Myth: Prison Data Manipulation

Claim 1: “The data seems to say that trans women offend in an identical way to men.”

Claim 2: “Trans women are convicted of sexual offenses at rates of about 1,177 per million—higher than men's 490 per million.”

Both claims cite the same UK Ministry of Justice prison data. Both are statistical manipulation.

You're comparing 76 people to 13,234 people using percentages. This hides the scale.

The Classroom Analogy

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

Does Classroom A “like chocolate more”? No. Classroom B has 174 times more chocolate lovers—it just looks smaller as a percentage because the classroom is massive.

Here's what they don't tell you about that “129 trans prisoners” number:

The MoJ only counts trans prisoners who:

• Have had a “case conference” (typically for sentences over 1 year)
• Have disclosed their trans status
• Don't have a Gender Recognition Certificate (GRC)

From the MoJ itself:

“Prisoners serving long sentences are more likely to be managed as a transgender prisoner than those on shorter sentences.”

Why this matters: Sexual offenses carry longer sentences. So you're only counting the subset of trans prisoners most likely to be sex offenders, then treating that as representative of all trans people.

From the BBC article:

“Trans prisoners on shorter sentences—who won't be in the survey—are less likely to be sex offenders.”

This is selection bias. It's like surveying people at a gym and concluding “most people exercise regularly.”

This number appears in no source document. Let me show you how it was likely manufactured:

They took: 76 ÷ 129 = 58.9%

Then multiplied by… something? The number is made up.

The actual calculation (if you wanted to do per-capita, which still has problems):

• UK trans population: ~48,000-262,000 (estimates vary)
• Trans women sex offenders in prison: 76
• Rate: 76 ÷ 48,000 × 1,000,000 = 1,583 per million

Wait, that's even higher! Except it's still wrong because:

• That 76 only counts a snapshot of who's in prison right now
• It excludes those with GRCs
• It's subject to the selection bias above
• Prison composition ≠ offense rates

Some also cite a 2011 Swedish study (Dhejne et al.) claiming it shows trans women have “male patterns of criminality.”

This study:

• Only examined 324 people who had full surgical transition
• Covered 1973-2003 (decades old)
• Found NO difference in the later cohort (1989-2003)
• Has been repeatedly misrepresented

See our full article: The Swedish Study: What It Actually Says

Short version: It doesn't support the claims being made, and the author has said so repeatedly.

What prison data shows: Of the prisoners we have right now, here's the breakdown.

What it doesn't show: How likely people are to commit crimes.

Why? Because you need:

• Total population size (not just prisoners)
• Reporting rates
• Conviction rates
• Sentencing patterns
• Who's still in prison vs. who's been released

The absurd example: “75% of maximum security prisoners are violent offenders, therefore 75% of people are violent.”

Obviously wrong—but that's the exact error being made.

When population sizes differ by 600+ times, per-capita rates become meaningless.

Watch what happens:

Six additional cases:

• Changes trans women rate by 7.9%
• Doesn't even round cisgender men rate

This is why per-capita fails with vastly different population sizes. Small absolute changes create huge percentage swings in the smaller group.

Ask the right question: “Who commits these crimes?”

That's the reality: 99.43% cisgender men, 0.57% trans women.

Now put it in population context:

• 76 out of ~59.6 million UK population = 0.000128%
• Or: 1 in 784,000 people

If you used the manipulated statistics to guide policy, you'd:

• Focus resources on 76 people
• While ignoring 13,234 people
• Because percentages looked scarier

This is how over-policing of minorities happens while the majority committing crimes gets ignored.

Note: Per-capita doesn't show “propensity to commit crimes.” It shows propensity to be convicted. These are very different things—but that's another discussion.

Using total convicted individuals (not just prisoners):

• 1.27 trans people per million have sexual offense convictions
• 222 cisgender men per million have sexual offense convictions

Even this comparison has problems (reporting rates, conviction rates), but it's far more valid than prison composition data.

Multiple peer-reviewed studies examining bathroom policies find:

• No increase in sexual assault in jurisdictions with trans-inclusive bathroom policies
• Trans people are victims of violence at twice the rate of cisgender people (UK data)

The original claim: “There are no recorded cases of a trans woman sexually assaulting a woman in a UK public toilet.”

Prison data doesn't refute this because:

• Doesn't specify location of offenses
• Doesn't distinguish offense types
• Can't tell us about bathrooms specifically

“There are lies, damned lies, and statistics.”

The claims rest on:

• Comparing percentages of vastly different sized groups (76 vs 13,234)
• Selection bias (only counting long-sentence prisoners)
• Fabricated statistics (“1,177 per million” appears nowhere)
• Confusing prison composition with crime rates (completely different calculations)
• Misusing per-capita (doesn't work with 600x population differences)

The reality:

• 99.43% of sexual offense prisoners are cisgender men
• 0.57% are trans women
• That's 1 in 784,000 people in the UK
• Research shows no safety concerns with trans-inclusive policies

When statistics are presented without proper context or with misleading comparisons between vastly different group sizes, they distort reality.

• Fair Play for Women submission to Parliament (2020)
• BBC Reality Check: “How many transgender inmates are there?” (2018)
• UK Ministry of Justice FOI data (2019-2020)
• Stop Hate UK: Transgender hate crime statistics
• American Academy of Pediatrics: Bathroom policy studies
• Springer: Safety and privacy research

This article aims to promote evidence-based policy discussion by clarifying common statistical manipulation. Good-faith questions about data interpretation are welcomed; weaponizing statistics to promote fear serves no one.