Severe Testing as a Basic Concept in a Neyman-Pearson Philosophy of Induction.
Despite the widespread use of key concepts of the Neyman-Pearson (N-P) statistical paradigm-type I and II errors, significance levels, power, confidence levels-they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N-P tests s...
| Published in: | British Journal for the Philosophy of Science Vol. 57; no. 2; pp. 323 - 358 |
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| Main Authors: | , |
| Format: | Article |
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University of Chicago Press
Jun2006
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=21810242&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 21810242 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00070882 BPL jtl: British Journal for the Philosophy of Science issn: 00070882 maglogo: N pubinfo: dt: Jun2006 vid: 57 iid: 2 pid: 415 pub: University of Chicago Press artinfo: ui: 21810242 10.1093/bjps/axl003 ppf: 323 ppct: 35 formats: tig: atl: Severe Testing as a Basic Concept in a Neyman-Pearson Philosophy of Induction. aug: au: Mayo, Deborah G. Spanos, Aris affil: Virginia Tech, Department of Philosophy, Blacksburg, VA 24061, USA su: Error Hypothesis Inference (Logic) Reasoning Philosophy sug: subj: Error Hypothesis Inference (Logic) Reasoning Philosophy ab: Despite the widespread use of key concepts of the Neyman-Pearson (N-P) statistical paradigm-type I and II errors, significance levels, power, confidence levels-they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N-P tests stem from unclarity and confusion, even among N-P adherents, as to how a test's (pre-data) error probabilities are to be used for (post-data) inductive inference as opposed to inductive behavior. We argue that the relevance of error probabilities is to ensure that only statistical hypotheses that have passed severe or probative tests are inferred from the data. The severity criterion supplies a meta-statistical principle for evaluating proposed statistical inferences, avoiding classic fallacies from tests that are overly sensitive, as well as those not sensitive enough to particular errors and discrepancies. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2006 holdings: @attributes: islocal: N |
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