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

===== The Transmisic Claims =====

**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 about**: Over representation of Trans Women in prisons for "sexual offenses," per-capita more likely to commit a "sexual offense," or a claim about being more likely/greater risk/threat to cisgender people because of prison numbers or "The Swedish Study."

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

===== What the Prison Data Actually Shows =====

^ Population ^ Total ^ Sex Offenders ^ Percentage ^
| Trans women prisoners | 129 | 76 | 58.9% |
| Cisgender men prisoners | 78,781 | 13,234 | 16.8% |
| Cisgender women prisoners | 3,812 | 125 | 3.3% |

===== The Sleight of Hand: Three Tricks in One =====

==== Trick #1: Comparing Percentages of Wildly Different Groups ====

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.

==== Trick #2: Ignoring Who Gets Counted ====

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."

==== Trick #3: The Completely Fabricated "1,177 per Million" ====

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**

==== Trick #4: Misusing the Swedish Study ====

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**: [[debunking-swedish-study|The Swedish Study: What It Actually Says]]

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


===== Why Prison Data Can't Tell You Crime Rates =====

**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.

===== The Per-Capita Problem =====

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

**Watch what happens**:

^ Group ^ Convictions ^ Population ^ Rate per 10,000 ^
| Trans women | 76 | 48,000 | 15.83 |
| Trans women (+6 more) | 82 | 48,000 | 17.08 |
| Cisgender men | 13,234 | 29,177,200 | 4.54 |
| Cisgender men (+6 more) | 13,240 | 29,177,200 | 4.54 |

**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.

===== What IS a Fair Comparison? =====

Ask the right question: **"Who commits these crimes?"**

{{ :sex-offense-convictions-by-group.png?600 |}}

^ Group ^ Sex Offenders ^ Percentage of All Sex Offenders ^
| Cisgender men | 13,234 | 99.43% |
| Trans women | 76 | 0.57% |
| **Total** | **13,310** | **100.00%** |

**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 Policy Disaster =====

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.

===== What About Actual Conviction Rates? =====

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.

===== What Research Actually Shows =====

**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

===== Summary =====

//"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.

===== Sources =====

  * 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.//
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