Severe Testing as a Basic Concept in a Neyman–Pearson Philosophy of Induction

By Deborah G. Mayo, Aris Spanos. British Journal for the Philosophy of Science, 2006

304 citations recorded by the source corpus.

Subfields: Artificial Intelligence, History and Philosophy of Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/0bcae0bb4e33d58e40ae570ff04370c5dfa4f009

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Citations in the corpus

Final Collapse of the Neyman-Pearson Decision Theoretic Framework and Rise of the neoFisherian (2009, background): “Mayo (1997) (2005), Mayo and Cox (2006), Mayo and Spanos (2006, 2009) give briefer, up-to-date summaries of her ideas.”
Philosophy and the practice of Bayesian statistics (2010, unknown): “See Raftery (1995) for a discussion of the value of posterior model probabilities in social science research and Gelman and Rubin (1995) for a discussion of their limitations, and Claeskens and Hjort (2008) for a general review of model selection.”
Similarity-based interference in sentence comprehension: Literature review and Bayesian meta-analysis (2017, background): “22 For example, Neyman (1955), cited in Mayo and Spanos (2006), writes: ‘‘[If] the chance of detecting the presence [of discrepancy from the null], .”
Where do statistical models come from? Revisiting the problem of specification (2006, methodology): “More formally, P1 has not passed a severe test, since its own statistical adequacy has not been established; see Mayo and Spanos ([13], [14]).”
Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules (2010, background): “Non-monotonic tests have been proposed as a way to increase power for unbiased tests, but Perlman and Wu (1999) (see especially the discussion of McDermott and Wang, 1999) argue against using these tests.”
When Does HARKing Hurt? Identifying When Different Types of Undisclosed Post Hoc Hypothesizing Harm Scientific Progress (2017, background): “…consideration of more modern insights about the extent to which evidence provides novel information in relation to hypotheses (Worrall, 1985, 2010, 2014) or the extent to which severe tests of those hypotheses provide diagnostic information (Mayo, 1991, 1996, 2008, 2010, 2014; Mayo & Spanos, 2006).”
Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations (2016, background): “Other approaches combine tests of the null with further calculations involving both null and alternative hypotheses (Rosenthal and Rubin 1994; Mayo and Spanos 2006); such calculations may, however, may bring with them further misinterpretations similar to those described above for power, as well as…”
Frequentist statistics as a theory of inductive inference (2006, background): “seems needed. Quite the contrary:choosing the more empirically adequate speciﬁcation gives reassurance that the calculated p-value is relevant for interpreting the evidence reliably. (Mayo and Spanos [19]). This might be justiﬁed more formally by regarding the speciﬁcation choice as an informal maxi”
Revisiting the omitted variables argument: Substantive vs. statistical adequacy (2006, background): “…testing, supplements the Neyman-Pearson decision rules with an evidential interpretation that renders the inference considerably more informative by distinguishing clearly the claims that are or are not warranted on the basis of the particular data; see Mayo and Spanos (2006) for further details.”
Akaike-type criteria and the reliability of inference: Model selection versus statistical model specification (2010, methodology): “…ofM(z) enables one to address problems (a)-(b), and deal with problems (I)-(V) by employing ascertainable error probabilities (pre-data and post-data) to evaluate the reliability and pertinence of inductive inferences, including the appraisal of substantive claims; see Mayo and Spanos (2006).”