Strategies for securing evidence through model criticism

Abstract
Some accounts of evidence regard it as an objective relationship holding between data and hypotheses, perhaps mediated by a testing procedure. Mayo’s error-statistical theory of evidence is an example of such an approach. Such a view leaves open the question of when an epistemic agent is justified in drawing an inference from such data to a hypothesis. Using Mayo’s account as an illustration, I propose a framework for addressing the justification question via a relativized notion, which I designate security , meant to conceptualize practices aimed at the justification of inferences from evidence. I then show how the notion of security can be put to use by showing how two quite different theoretical approaches to model criticism in statistics can both be viewed as strategies for securing claims about statistical evidence
Keywords Evidence  Statistics  Robustness  Mis-specification testing  Error-statistics  Justification  Security  Statistical models
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DOI 10.1007/s13194-011-0022-x
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References found in this work BETA
Knowledge and Belief.Jaakko Hintikka - 1962 - Ithaca: Cornell University Press.
Error and the Growth of Experimental Knowledge.Deborah G. Mayo - 1996 - International Studies in the Philosophy of Science 15 (1):455-459.
The Book of Evidence.Peter Achinstein - 2001 - Oxford University Press.
The Nature of Epistemic Space.David J. Chalmers - 2011 - In Andy Egan & Brian Weatherson (eds.), Epistemic Modality. Oxford University Press.
Epistemic Possibilities.Keith DeRose - 1991 - Philosophical Review 100 (4):581-605.

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Strategies for Securing Evidence Through Model Criticism.Kent W. Staley - 2012 - European Journal for Philosophy of Science 2 (1):21-43.
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