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


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.

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Many people have contributed along the way to identifying the problem of Simpson’s paradox in data analysis, investigating it empirically, as well as devising methods to mitigate its effects. These people include Nathan Hodas, Farshad Kooti, Keith Burghardt, Philipp Singer, Emilio Ferrara, Peter Fennell, Nazanin Alipourfard. This work was funded, in part, by Army Research Office under contract W911NF-15-1-0142.

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Correspondence to Kristina Lerman.

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Lerman, K. Computational social scientist beware: Simpson’s paradox in behavioral data. J Comput Soc Sc 1, 49–58 (2018).

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  • Statistics
  • Simpson’s paradox
  • Survivorship bias
  • Ecological fallacy
  • Heterogeneity