Most of the methods we use in criminology to infer relationships are based on mean values of distributions. This essay explores the historical origins of this issue and some counterproductive consequences: relying too heavily on sampling as a means of ensuring “statistical significance”; ignoring the implicit assumptions of regression modeling; and assuming that all data sets reflect a single mode of behavior for the entire population under study. The essay concludes by suggesting that we no longer “make do” with the standard methodologies used to study criminology and criminal justice, and recommends developing categories that more accurately reflect behavior and groupings than the ones we currently use; looking at alternative sources of data, including qualitative data such as narrative accounts; and developing alternative methods to extract and analyze the data from such sources.

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In the 1830s Simron-Denis Poisson developed the distribution that bears his name, basing it on the binomial distribution. He used it to show how the inherent variance in jury decisions affected the inferences that could be made about the probability of conviction in French courts. In recent years there have been a number of examples where researchers have either ignored or forgotten this inherent variance, and how operations research, in particular mathematical modeling, can be used to incorporate this variance in analyses. These are described in this paper, as well as other contributions made by operations research to the study of crime and criminal justice.

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