The philosophy of Bayes’ factors and the quantification of statistical evidence

By Richard D. Morey, Jan-Willem Romeijn, Jeffrey N. Rouder. 2016

289 citations recorded by the source corpus.

A core aspect of science is using data to assess the degree to which data provide evidence for competing claims, hypotheses, or theories. Evidence is by definition something that should change the credibility of a claim in a reasonable person’s mind. However, common statistics, such as significance testing and confidence intervals have no interface with concepts of belief, and thus it is unclear how they relate to statistical evidence. We explore the concept of statistical evidence, and how it can be quantified using the Bayes factor. We also discuss the philosophical issues inherent in the use of the Bayes factor.

Subfields: Artificial Intelligence, History and Philosophy of Science

View Paper: https://www.semanticscholar.org/paper/ebbe48ef4ce8d6ba8900bff72ebba9cb1a1664fb

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In Artificial Intelligence

In History and Philosophy of Science

Citations in the corpus

Bayes factor design analysis: Planning for compelling evidence (2016, methodology): “After both models have been specified so that they make predictions, the observed data can be used to assess each models’ predictive adequacy (Morey et al., 2016; Wagenmakers et al., 2006; Wagenmakers et al., 2016).”
Teaching Bayes’ Theorem: Strength of Evidence as Predictive Accuracy (2018, background): “In words, we say that “strength of evidence for a parameter value is precisely the relative gain in predictive accuracy when conditioning on it” (see Morey, Romeijn, and Rouder 2016).”
Improving Methodological Standards in Behavioral Interventions for Cognitive Enhancement (2019, methodology): “We also note that this practice of full reporting applies also to alternative methods to quantify statistical evidence, such as the recently proposed Bayes factors (Morey et al. 2016; Rouder et al. 2009).”