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Coherent Hypothesis Testing

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  • Victor Fossaluza
  • Rafael Izbicki
  • Gustavo Miranda da Silva
  • Luís Gustavo Esteves

Abstract

Multiple hypothesis testing, an important quantitative tool to report the results of scientific inquiries, frequently leads to contradictory conclusions. For instance, in an analysis of variance (ANOVA) setting, the same dataset can lead one to reject the equality of two means, say μ1 = μ2, but at the same time to not reject the hypothesis that μ1 = μ2 = 0. These two conclusions violate the coherence principle introduced by Gabriel in 1969, and lead to results that are difficult to communicate, and, many times, embarrassing for practitioners of statistical methods. Although this situation is common in the daily life of statisticians, it is usually not discussed in courses of statistics. In this work, we enrich the teaching and discussion of this important topic by investigating through a few examples whether several standard test procedures are coherent or not. We also discuss the relationship between coherent tests and measures of support. Finally, we show how a Bayesian decision-theoretical framework can be used to build coherent tests. These approaches to coherence enlighten when such property is appealing in multiple testing and provide means of obtaining it.

Suggested Citation

  • Victor Fossaluza & Rafael Izbicki & Gustavo Miranda da Silva & Luís Gustavo Esteves, 2017. "Coherent Hypothesis Testing," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 242-248, July.
    • Handle: RePEc:taf:amstat:v:71:y:2017:i:3:p:242-248
    DOI: 10.1080/00031305.2016.1237893
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    Cited by:

    1. Kenneth Rice & Tyler Bonnett & Chloe Krakauer, 2020. "Knowing the signs: a direct and generalizable motivation of two‐sided tests," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 411-430, February.
    2. Sander Greenland, 2023. "Divergence versus decision P‐values: A distinction worth making in theory and keeping in practice: Or, how divergence P‐values measure evidence even when decision P‐values do not," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(1), pages 54-88, March.

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