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Aspects of Truth: Statistics, Bias, and Confounding

Published online by Cambridge University Press:  21 June 2016

Leon F. Burmeister ,
Sam Sheps  and
David Birnbaum
Leon F. Burmeister
Affiliation:
Department of Health Care and Epidemiology, the University of British Columbia, Vancouver, British Columbia
Sam Sheps*
Affiliation:
Department of Health Care and Epidemiology, the University of British Columbia, Vancouver, British Columbia
David Birnbaum
Affiliation:
Department of Health Care and Epidemiology, the University of British Columbia, Vancouver, British Columbia
*
Department of Health Care and Epidemiology, 5804 Fairview Ave., Vancouver, British Columbia, V6T 1Z3
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Extract

Conclusions drawn from research can be true, can be distortions resulting from bias or confounding, or can be untrue because of random chance. The essential role of epidemiology is to provide an approach to the appraisal of data concerning health and disease in order to discern the truth.

The first article in this series discussed issues of validity and reliability in relation to diagnostic testing. A second article considered statistical concepts in the special case of inferring that a null hypothesis is true. This article will consider related aspects of research design, analysis, and the logic of scientific thought, while the next article in the series will discuss the role of confidence intervals in the analysis and interpretation of data.

Type
Statistics for Hospital Epidemiology
Information
Infection Control & Hospital Epidemiology , Volume 13 , Issue 7 , July 1992 , pp. 418 - 420
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1992

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