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Several recent papers in the field of Evolution and Human Behavior rely on aggregate data when testing their hypothesis on adaptations in humans. This is perhaps most notably the case for studies on pathogen stress, (e.g., DeBruine et al., 2010; Thornhill and Fincher, 2011; Fincher and Thornhill, 2012). These studies predominantly rely on cross-cultural correlations and present p-values in support of their hypotheses. In this opinion article, I demonstrate why p-values can be questionable in this context. I do not wish to single out a particular research area, as the misinterpretation of p in this context seems relatively widespread. But for the purpose of this opinion article I will largely draw on examples from work relating to pathogen stress, as this research area most prominently appears to rely on aggregated cross-cultural data. I also want to stress that this is not a general critique of p-value usage or frequentist statistics (e.g., Johnson, 1999; Anderson et al., 2000; Goodman, 2008; Ziliak and McCloskey, 2008; Wetzels et al., 2011), but rather a critique on the reliance on p-values when using macrolevel data in cases where the sample closely matches the entire range of possible observations. This opinion article is also not a critique of reliance on macrolevel data per se, or of a research programme in particular, but focuses on one particular aspect: statistical inference from macrolevel data when a sample closely matches the entire population.

Much ado about p. What does a p-value mean when testing hypotheses with aggregated cross-cultural data in the field of evolution and human behavior?