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Comparing apples to porcupines — using trans data
If you wish to use any data with respects to transgender people several caveats need to be applied. First, trans people make up a small percentage of any given population meaning that any outliers within that group will be magnified within the data. Second, trans identities are self-reported, and thus all trans related data is both self-selective and based on people willing to provide that data. Third, using that data to compare it with other external datasets which use different collating methods is tenuous at best and impossible at worst. Combined, this makes using any trans related dataset a matter of comparing apples to porcupines when extrapolated out to other data sources. You can compare and apple to a porcupine, show they are biological objects in the world, may hurt you if handled the wrong way, yet that is just about it.
Bad data science is de rigueur for those trying to prove that trans women are a threat to “real” women in society. Gender critics have made it ala mode to spam timelines with memes of trans female criminals or cross-dressing men, stripping the images and blurbs of all context and underlying data. They promote anti-trans propaganda the same way the National Socialists did with regards to the Jews, and those who know no better accept them as facts.
