Computational social scientist beware: Simpson’s paradox in behavioral data

Kristina Lerman ()
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Kristina Lerman: USC Information Sciences Institute

Journal of Computational Social Science, 2018, vol. 1, issue 1, No 4, 49-58

Abstract: Abstract Observational data about human behavior are often heterogeneous, i.e., generated by subgroups within the population under study that vary in size and behavior. Heterogeneity predisposes analysis to Simpson’s paradox, whereby the trends observed in data that have been aggregated over the entire population may be substantially different from those of the underlying subgroups. I illustrate Simpson’s paradox with several examples coming from studies of online behavior and show that aggregate response leads to wrong conclusions about the underlying individual behavior. I then present a simple method to test whether Simpson’s paradox is affecting results of analysis. The presence of Simpson’s paradox in social data suggests that important behavioral differences exist within the population, and failure to take these differences into account can distort the studies’ findings.

Keywords: Statistics; Simpson’s paradox; Survivorship bias; Ecological fallacy; Heterogeneity (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s42001-017-0007-4

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