Toward Error-Statistical Principles of Evidence in Statistical Inference

The context for this research is statistical inference, the process of making predictions or inferences about a population from observation and analyses of a sample. In this context, many researchers want to grasp what inferences can be made that are valid, in the sense of being able to uphold or justify by argument or evidence. Another pressing question among users of statistical methods is: how can spurious relationships be distinguished from genuine ones? Underlying both of these issues is the concept of evidence. In response to these (and similar) questions, two questions I work on in this essay are: (1) what is a genuine principle of evidence? and (2) do error probabilities have more than a long-run role? Concisely, I propose that felicitous genuine principles of evidence should provide concrete guidelines on precisely how to examine error probabilities, with respect to a test's aptitude for unmasking pertinent errors, which leads to establishing sound interpretations of results from statistical techniques. The starting point for my definition of genuine principles of evidence is Allan Birnbaum's confidence concept, an attempt to control misleading interpretations. However, Birnbaum's confidence concept is inadequate for interpreting statistical evidence, because using only pre-data error probabilities would not pick up on a test's ability to detect a discrepancy of interest (e.g., "even if the discrepancy exists" with respect to the actual outcome. Instead, I argue that Deborah Mayo's severity assessment is the most suitable characterization of evidence based on my definition of genuine principles of evidence.

Name thesis:degree_name

Master of Arts

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Philosophy

Department dc:contributor.department

Philosophy

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2014

Author dc:creator

• Jinn, Nicole Mee-Hyaang

Chair dc:contributor.committeechair

• Mayo, Deborah G.

Committee members dc:contributor.committeemember

• Pitt, Joseph C.
• Patton, Lydia K.

• Statistical Inference
• Evidential/Inferential Interpretations
• Evidence
• Sampling distributions
• Likelihood Principle
• Bayesian methods
• Error Statistics
• Frequentist methods
• Philosophy of Statistics
• Statistics Education

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:2882

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/48420