A Comparison of Classical and Bayesian Statistical Analysis in Operational Testing.

Abstract

This research is devoted to investigating how Bayesian statistical analysis differs from classical statistical analysis in the context of operational testing. The specific aspects of operational testing which are considered are the power resulting from a hypothesis test and the expected loss, or risk, resulting from a decision. First it is shown that it is quite difficult to develop a meaningful measure of comparison between Bayesian and classical analysis in the framework of hypothesis testing. Using the power of the hypothesis test as a measure of comparison, it is shown that under certain conditions classical statistical procedures lead to more powerful tests than Bayesian procedures. It is then shown that Bayesian statistical procedures are superior to classical procedures in the framework of minimizing expected loss or risk. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1977
Accession Number
ADA051260

Entities

People

  • Philip Vincent Coyle

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Bayes Theorem
  • Bayesian Inference
  • Data Science
  • Distribution Theory
  • Information Science
  • Knowledge Management
  • Operations Research
  • Probability
  • Probability Distributions
  • Random Variables
  • Standards
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Decision Theory
  • Statistics
  • Test And Evaluation
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Aerospace Test and Evaluation
  • Approximation Theory.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms