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Activity Number: 51 - BFF: Innovation in Statistical Foundations
Type: Topic Contributed
Date/Time: Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #322446
Title: Warrant and Severity in Statistical Inference
Author(s): Ruobin Gong*
Companies: Rutgers University
Keywords: falsifiability; modus tollens; hypothesis testing; confidence distribution; statistical inference; coherence

Severity (Mayo, 2018) is the degree of warrant accorded to an inferential claim resulting from an inferential strategy in relation to a body of evidence. Extending the Popperian notion of falsifiability, severity seeks to establish a stochastic version of modus tollens, as a measure of strength of probabilistic inference. If the available evidence leads a strategy to infer something about the world, then were it not the case, would the strategy still have inferred it?

I discuss the formulation of warrant and severity in general statistical inference tasks of both the frequentist and Bayesian traditions. I investigate the properties of the warrant function, including coherence and informativeness, and demonstrate its assessment and interpretation with examples. A connection with significance function (Fraser, 1991) and confidence distribution (Xie & Singh, 2013) is drawn. This vocabulary may enable the assessment of warrant and severity in a wide range of modern applications that call for evidence-based scientific decision making.

Authors who are presenting talks have a * after their name.

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