The aggregation paradox for statistical rankings and nonparametric tests

Haikady N Nagaraja and Shane Sanders

PLOS ONE, 2020, vol. 15, issue 3, 1-21

Abstract: The relationship between social choice aggregation rules and non-parametric statistical tests has been established for several cases. An outstanding, general question at this intersection is whether there exists a non-parametric test that is consistent upon aggregation of data sets (not subject to Yule-Simpson Aggregation Paradox reversals for any ordinal data). Inconsistency has been shown for several non-parametric tests, where the property bears fundamentally upon robustness (ambiguity) of non-parametric test (social choice) results. Using the binomial(n, p = 0.5) random variable CDF, we prove that aggregation of r(≥2) constituent data sets—each rendering a qualitatively-equivalent sign test for matched pairs result—reinforces and strengthens constituent results (sign test consistency). Further, we prove that magnitude of sign test consistency strengthens in significance-level of constituent results (strong-form consistency). We then find preliminary evidence that sign test consistency is preserved for a generalized form of aggregation. Application data illustrate (in)consistency in non-parametric settings, and links with information aggregation mechanisms (as well as paradoxes thereof) are discussed.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0228627

DOI: 10.1371/journal.pone.0228627

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