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Statisticism: How Cluster-Thinking About Data Creates Blind Spots

benjaminrosshoffman.com · 3,750 words · saved by 1 readers

There is an epistemic stance, common among academics in quantitative fields, academics who wish they were in quantitative fields, and independent scholars who do not wish to decorrelate too much from the academic mainstream by communicating in an incompatible dialect, that treats statistical convergence as the gold standard of evidence. If many indicators point the same direction, the signal is real. Call this statisticism. It converges on truth when your instruments have independent errors. It diverges from truth when they share a systematic distortion, because then convergence is what the distortion looks like. The following example illustrates a case where it fails, and why. The US homicide rate doubled between 1960 and 1980, then fell by more than half between 1991 and 2014. I argued that the fall is mostly a medical artifact: trauma surgery vastly improved, so the same rate of shootings produced fewer deaths. I constructed an adjusted trend line using two independent data sources

There is an epistemic stance, common among academics in quantitative fields, academics who wish they were in quantitative fields, and independent scholars who do not wish to decorrelate too much from the academic mainstream by communicating in an incompatible dialect, that treats statistical convergence as the gold standard of evidence. If many indicators point in the same direction, the signal is real. Call this statisticism. It converges on truth when your instruments have independent errors. It diverges from truth when they share a systematic distortion, because then convergence is what the

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