On Combining Independent Significance Tests

Abstract

How to combine the results of k independent tests of significance has long been an important problem in statistics. The problem can arise, for example, in such diverse situations as when tests on the mean survival time after diagnosis of a terminal disease are made on k groups of patients in different hospitals, or when the sets of observations in k cells of an ANOVA table are separately tested for normality. An important feature of such tests is that often the individual sample sizes will be small, so that asymptotic results will not necessarily be valid. We suggest below that they might even be misleading in some situations.

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Document Details

Document Type
Technical Report
Publication Date
Jul 24, 1991
Accession Number
ADA239660

Entities

People

  • F. J. O'reilly
  • Michael A. Stephens

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Computing-Related Activities
  • Data Analysis
  • Data Science
  • Data Sets
  • Distribution Functions
  • Efficiency
  • Hospitals
  • Information Science
  • Military Research
  • Monte Carlo Method
  • New York
  • Normal Distribution
  • Observation
  • Statistics
  • Survival
  • United States
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Trauma or Military Medicine