Criminal Georg skews recidivism statistics




Have you ever seen those concerning statistics about criminal recidivism? Like: 44% are re-arrested within a year, and 83% within nine years (source: this Department of Justice report).

I’d seen those statistics before, and been concerned. There’s a great case for shortening prison sentences for deterrence reasons, because likelihood of punishment is much more deterring than severity, but at least prison incapacitates criminals from plundering society while they’re imprisoned. Why hasten prison release if they’ll be back soon anyway? “Once a criminal, always a criminal?”, asks one headline about recidivism.

But today I learned that there’s a huge caveat to those statistics. The more often you go to prison, the more you’re counted in recidivism statistics.

Consider five people who go to prison. Four of them never commit another crime, but one of them was Criminal Georg, who is imprisoned ten times. Out of the fourteen prison sentences (ten for Georg, four for the others), nine of them are followed by recidivism (Georg’s first nine). The proportion of these people who are serial criminals is 20%, but the recidivism rate is 64%.

When considering people rather than prison releases, the recidivism rate is lower than I thought.

see this thread for more examples

Thank you, I hadn’t seen it and it’s a great resource!

I knew I’d seen this pattern before, but I didn’t have a name for it. The linked post by Elizabeth Wrigley-Field tells me it’s called “length-biased sampling.”

The mentions several examples with real-world importance, incl. the recidivism one, and argues the concept should be more widely known.

(It also makes an argument that “length-biased sampling is the deep structure of nested categories” which sounds interesting but which I am not awake enough rn to wrap my head around)


#fun with statistics #(well maybe not ”fun”) #the more you know #prison cw

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