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arXiv:1311.0081stat.ME

To P or not to P: on the evidential nature of P-values and their place in scientific inference

Michael J. Lew

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Abstract

The customary use of P-values in scientific research has been attacked as being ill-conceived, and the utility of P-values has been derided. This paper reviews common misconceptions about P-values and their alleged deficits as indices of experimental evidence and, using an empirical exploration of the properties of P-values, documents the intimate relationship between P-values and likelihood functions. It is shown that P-values quantify experimental evidence not by their numerical value, but through the likelihood functions that they index. Many arguments against the utility of P-values are refuted and the conclusion is drawn that P-values are useful indices of experimental evidence. The widespread use of P-values in scientific research is well justified by the actual properties of P-values, but those properties need to be more widely understood.

arXiv comments
31 pages, 9 figures and R code
Journal reference
DOI
Subjects
stat.MEMethodology (Statistics)

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Linked by 1 identifiable commenter in 3 comments across 3 threads, from Nov 2015 to Nov 2016.

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Discussed in comments3 threads

    • Unless I am misunderstanding something, their figure 3 seems to be plotting effect size vs p-value... So all it would be showing is that they had more data from lung adenocarcinomas (ie sample size is larger for that cancer type). It isn't 100% clear to me if they shared the…

      nonbelView on HNParent context

    • >"I’ve come to think that the most fundamental problem with p-values is that no one can really say what they are." Not surprising. The correct interpretation of p-values was not discovered until ~2 years ago. If you search around the internet you will find out that, according to…

      nonbelView on HNParent context

  1. The Great Statistical Schism 1 comment
    • Check out this paper: http://arxiv.org/abs/1311.0081 Your interpretation is fine up until we start using a cutoff and talking about significant or not significant results, whether the null hypothesis is true, etc. It is using significance levels that introduces the nonsense.

      nonbelView on HNParent context

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