Rajiv Sethi published an article recently, A Fallacy of Composition, that again addresses bias and police violence. This time, stimulated by an article by Peter Moskos, he looks at the effects of grouping data and how that can obscure or reveal patterns:

But Moskos offers another, quite different reason why bias in individual incidents might not be detected in aggregate data: large regional variations in the use of lethal force.

To illustrate his point, Sethi constructs data for two cities that each show discrimination, but when their data are combined, the bias disappears.

Although not mentioned by name in Sethi’s article, the effect being discussed is sometimes labeled Simpson’s Paradox.

The appearance or disappearance of bias when data is combined or divided is counter intuitive. It will help you to work through some examples to see how it works and I may come back here to write more about this in the future.

For now, I just want to give you Simpson’s Paradox as a search term.

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