Surprise and evidence in statistical model checking
| Abstract | There is considerable confusion about the role of p-values in statistical model checking. To clarify that point, I introduce the distinction between measures of surprise and measures of evidence which come with different epistemological functions. I argue that p-values, often understood as measures of evidence against a null model, do not count as proper measures of evidence and are closer to measures of surprise. Finally, I sketch how the problem of old evidence may be tackled by acknowledging the epistemic role of surprise indices. | |||||||||
| Keywords | No keywords specified (fix it) | |||||||||
| Categories | ||||||||||
| Options |
|
|||||||||
Download options| PhilPapers Archive |
Upload a copy of this paper Check publisher's policy on self-archival Papers currently archived: 5,709 |
| External links |
  Try with proxy. |
| Through your library | Only published papers are available at libraries |
Similar books and articlesElliott Sober (1998). To Give a Surprise Exam, Use Game Theory. Synthese 115 (3):355-373.
Kent Staley (2012). Strategies for Securing Evidence Through Model Criticism. European Journal for Philosophy of Science 2 (1):21-43.
Steven Gimbel (1999). Peirce Snatching: Towards a More Pragmatic View of Evidence. Erkenntnis 51 (2-3):207-231.
Stephan Merz (2002). Model Checking Techniqes for the Analysis of Reactive Systems. Synthese 133 (1-2):173 - 201.
Hillel D. Braude (2009). Clinical Intuition Versus Statistics: Different Modes of Tacit Knowledge in Clinical Epidemiology and Evidence-Based Medicine. Theoretical Medicine and Bioethics 30 (3):181-198.
J. V. Howard (2009). Significance Testing with No Alternative Hypothesis: A Measure of Surprise. Erkenntnis 70 (2):253 - 270.
Analytics
Monthly downloads |
Added to index2009-01-28Total downloads12 ( #93,475 of 549,697 )Recent downloads (6 months)2 ( #37,450 of 549,697 )How can I increase my downloads? |
My notes
Discussion
