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.”

Any claim suggesting:

• Over-representation of trans women in prisons for “sexual offenses”
• Trans women are per-capita more likely to commit “sexual offenses”
• Trans women pose a greater risk/threat to cisgender people
• Based on prison composition statistics or “The Swedish Study”

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All of these claims rely on the same statistical manipulation techniques and misrepresentations of studies and fact, which we'll break down below.

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

Beyond selection bias, tiny sample sizes create another critical problem: statistical volatility.

With only 129 trans prisoners recorded, any small fluctuation dramatically changes percentages. Six additional cases shifts the rate by 7.9%. Sixty additional cases? A complete inversion of the narrative.

Any competent statistician would flag this as unsuitable for policy conclusions. Real research on rare populations uses confidence intervals—ranges showing uncertainty—which are conspicuously absent here.

Without confidence intervals, you're presenting a point estimate (76 people) as if it's a stable truth, when it's actually volatile. It's like measuring someone's weight once and declaring it their “true weight” without acknowledging measurement error. The number itself is unreliable.

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.

People are cognitively wired to notice percentages more than population scales. When told “58.9% of trans prisoners vs. 16.8% of cis male prisoners are sex offenders,” our brains fixate on the percentages while ignoring the silent base rate: that we're comparing 76 people to 13,234 people.

This isn't accidental. The base-rate fallacy—ignoring population frequency in favor of specific numbers—is how the manipulation becomes persuasive. A detailed breakdown follows below.

The claims are often accompanied by infographics using icon arrays—rows of small human figures representing per-million rates. The graphic referenced at the start of this article uses this technique to devastating effect.

Here's the sleight of hand:

What the graphic shows: Rates (per million), not counts.

What your brain perceives: A “dense array of people” implying a large absolute number of dangerous individuals.

The reality: Those densely packed icons represent fewer than 60 people total—a population so small that statistical noise dominates the picture.

When you see 300 icons and are told “this represents trans women sex offenders,” your emotional brain registers: “Wow, that's a lot of dangerous people.” Your rational brain never catches up to the lie because the visual has already anchored your intuition.

This is why icon arrays are propaganda tools when applied to rare populations and rates. They conflate two completely different things:

• Counts (absolute numbers: “76 people”) communicate scale honestly
• Rates (per million) communicate frequency, but when visualized as icon counts, they lie about scale

The correct visualization: A single icon representing all 76 trans women sex offenders, surrounded by 13,234 icons for cisgender men. That's what honesty looks like.

Instead, the graphic uses per-million rates displayed as icons, manufacturing visual horror from statistical smoke.

Before we analyze the numbers, we need to understand what “sexual offense” actually means in UK law. It's an extremely broad category.

Under UK law, “sexual offense” includes:

Serious violent offenses:

• Rape and attempted rape
• Sexual assault and attempted sexual assault
• Child sexual abuse offenses
• Indecent assault

Non-violent and non-contact offenses:

• Possession of indecent images
• Distribution of indecent images
• Making/downloading indecent images
• Voyeurism
• Exposure/indecent exposure
• Outraging public decency

Sex work-related offenses:

• Soliciting for prostitution
• Loitering for prostitution
• Controlling prostitution
• Keeping a brothel
• Advertising sexual services

Other sexual offenses:

• Sexual activity in a public toilet
• Breach of Sexual Harm Prevention Order
• Failure to register as sex offender
• Various consent and age-of-consent related charges

When someone hears “76 trans women sex offenders,” most people picture rapists and child abusers. But that category could include:

• Someone convicted of soliciting for sex work
• Someone who failed to register an address change as a former offender
• Someone caught with downloaded pornography
• Someone convicted of public indecency
• Someone convicted of rape or sexual assault

These are NOT equivalent crimes, yet they're lumped together in the statistics. Without a breakdown, we cannot know the distribution.

Trans women, particularly trans women of color, are disproportionately pushed into survival sex work due to:

• Employment discrimination
• Housing discrimination
• Family rejection
• Lack of economic opportunities

Research shows:

• Trans women are far more likely to engage in sex work than cisgender women
• Trans women in sex work face higher rates of criminalization
• Many “sexual offense” convictions are actually sex work-related charges

This means the “sexual offense” statistics may be inflated by survival crimes, not violent offenses. Without detailed breakdowns, we cannot determine how much.

Using the term “sexual offense” without breakdown is loaded language designed to invoke fear. It's an appeal to emotion that relies on people assuming all “sex offenders” are rapists.

The reality: The category ranges from rape to soliciting, and treating them as equivalent is intellectually dishonest.

What we know: The MoJ data tells us 76 trans women prisoners had sexual offense convictions. What we don't know: The distribution of those offenses across the wide spectrum of UK sexual offense law.

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.

All five tricks rely on the same cognitive bias: the base-rate fallacy—ignoring the overall frequency of something in a population while fixating on a percentage that seems dramatic in isolation.

When you hear “58.9% of trans prisoners are sex offenders,” your brain registers the percentage as alarming.

What disappears: the base rate—that you're comparing 76 people to 13,234 people, and that trans women comprise just 0.57% of all sex offense convictions.

This happens because percentages feel more vivid than population scale. Your brain excels at processing “58.9%” but struggles to weight it against “vastly outnumbered population.” The tricks weaponize this gap.

Restore the base rate by asking: “Of all sex offenders, what percentage are trans women?” instead of “What percentage of trans prisoners are sex offenders?” Suddenly, the feared narrative collapses.

The sections below demonstrate this fallacy mathematically.

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

The data manipulation extends to how categories themselves are defined—and critically, how those definitions shift to serve the narrative.

When defining the male prisoner rate: “males (including other trans identities)“—a *broader* category that lowers the percentage.

When defining the trans prisoner rate: “excluding other trans identities”—a *narrower* category that raises the percentage.

This is not inconsistency; it's deliberate. Each definition is chosen specifically to move the rate in the desired direction. Call it what it is: analytical laundering. It's not reasoning; it's manipulation dressed as consistency.

Fair comparison requires consistent categorization. Either include “other trans identities” in both calculations, or exclude them from both—but not whichever serves your conclusion.

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.