Race and Police Shootings: Why Data Sampling Matters

When reading headlines about findings from data, always ask: “To what population does this conclusion apply?” Brian D’Alessandro explains eloquently why sampling matters.

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This is a guest post by Brian D’Alessandro, who daylights as the Head of Data Science at Zocdoc and as an Adjunct Professor with NYU’s Center for Data Science. When not thinking probabilistically, he’s drumming with the indie surf rock quarter Coastgaard.

I’d like to address the recent study by Roland Fryer Jr  from Harvard University, and associated NY Times coverage, that claims to show zero racial bias in police shootings. While this paper certainly makes an honest attempt to study this very important and timely problem, it ultimately suffers from issues of data sampling and subjective data preparation. Given the media attention it is receiving, and the potential policy and public perceptual implications of this attention, we as a community of data people need to comb through this work and make sure the headlines are consistent with the underlying statistics.

First thing’s first: is there really zero…

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