Frequentist statistics as a theory of inductive inference

By Deborah G. Mayo, D. R. Cox. 2006

112 citations recorded by the source corpus.

After some general remarks about the interrelation between philosophical and statistical thinking, the discussion centres largely on significance tests. These are defined as the calculation of $p$-values rather than as formal procedures for ``acceptance'' and ``rejection.'' A number of types of null hypothesis are described and a principle for evidential interpretation set out governing the implications of $p$-values in the specific circumstances of each application, as contrasted with a long-run interpretation. A variety of more complicated situations are discussed in which modification of the simple $p$-value may be essential.

Subfields: History and Philosophy of Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1bf4dbd90d885fffa27010c1cd9d6d8ae59053ed

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In History and Philosophy of Science

In Statistics and Probability

Citations in the corpus

Testing hypotheses via a mixture estimation model (2014, background): “INTRODUCTION Hypothesis testing is one of the central problems of statistical inference and a dramatically differentiating feature of classical and Bayesian paradigms (Neyman and Pearson, 1933; Berger and Sellke, 1987; Casella and Berger, 1987; Gigerenzer, 1991; Berger, 2003; Mayo and Cox, 2006; Gel”
Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations (2016, methodology): “Thus, from this frequentist perspective, P -values do not overstate evidence and may even be considered as measuring one aspect of evidence (Cox 1977, 1982; Lehmann 1986; Senn 2001, 2002a; Mayo and Cox 2006), with 1 − P measuring evidence against the model used to compute the P -value.”
Human life is unlimited – but short (2017, background): “For a discussion of questions related to this, see Mayo and Cox (2006).”
Akaike-type criteria and the reliability of inference: Model selection versus statistical model specification (2010, background): “This amounts to establishing the smallest (largest) discrepancy ≥ 0 from 0 warranted by data z0, associated with the N-P decision to accept (reject) 0; see Mayo and Spanos (2006), Mayo and Cox (2006).”
Power-Law Distributions in Empirical Data (2007, unknown): “For a general discussion of the interpretation of p-values, see Mayo and Cox (2006).”