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The Safe, the Sensitive, and the Severely Tested: A Unified Account

Gardiner, Georgi and Zaharatos, Brian (2022) The Safe, the Sensitive, and the Severely Tested: A Unified Account. [Preprint]

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Abstract

This essay presents a unified account of safety, sensitivity, and severe testing. S’s belief is safe iff, roughly, S could not easily have falsely believed p, and S’s belief is sensitive iff were p false S would not believe p. These two conditions are typically viewed as rivals but, we argue, they instead play symbiotic roles. Safety and sensitivity are both valuable epistemic conditions, and the relevant alternatives framework provides the scaffolding for their mutually supportive roles. The relevant alternatives condition holds that a belief is warranted only if the evidence rules out relevant error possibilities. The safety condition helps categorise relevant from irrelevant possibilities. The sensitivity condition captures ‘ruling out’.

Safety, sensitivity, and the relevant alternatives condition are typically presented as conditions on warranted belief or knowledge. But these properties, once generalised, help characterise other epistemic phenomena, including warranted inference, legal verdicts, scientific claims, reaching conclusions, addressing questions, warranted assertion, and the epistemic force of corroborating evidence.

We introduce and explain Mayo’s severe testing account of statistical inference. A hypothesis is severely tested to the extent it passes tests that probably would have found errors, were they present. We argue Mayo’s account is fruitfully understood using the resulting relevant alternatives framework. Recasting Mayo’s condition using the conceptual framework of contemporary epistemology helps forge fruitful connections between two research areas—philosophy of statistics and the analysis of knowledge—not currently in sufficient dialogue. The resulting union benefits both research areas.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Gardiner, Georgigeorgicloud9@gmail.com0000-0002-9343-5155
Zaharatos, Brianbrian.zaharatos@colorado.edu0000-0002-1537-078X
Additional Information: Published in Synthese. Cheat Sheet available here: https://drive.google.com/file/d/11HHc_ckh9FRyRBr6owWOL0mGRVp9D5mi/view
Keywords: Statistical inference, severe testing, error detection in science, safety, sensitivity, relevant alternatives framework, Deborah Mayo
Subjects: General Issues > Data
Specific Sciences > Mathematics > Epistemology
Specific Sciences > Mathematics > Methodology
Specific Sciences > Mathematics > Practice
General Issues > Confirmation/Induction
General Issues > Evidence
General Issues > Experimentation
Specific Sciences > Probability/Statistics
General Issues > Social Epistemology of Science
General Issues > Theory/Observation
Depositing User: Dr. Georgi Gardiner
Date Deposited: 24 Jul 2022 11:58
Last Modified: 24 Jul 2022 11:58
Item ID: 20960
Subjects: General Issues > Data
Specific Sciences > Mathematics > Epistemology
Specific Sciences > Mathematics > Methodology
Specific Sciences > Mathematics > Practice
General Issues > Confirmation/Induction
General Issues > Evidence
General Issues > Experimentation
Specific Sciences > Probability/Statistics
General Issues > Social Epistemology of Science
General Issues > Theory/Observation
Date: 2022
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/20960

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