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Am J Physiol Endocrinol Metab 286: E495-E501, 2004; doi:10.1152/ajpendo.00484.2003
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INVITED REVIEW

Ten categories of statistical errors: a guide for research in endocrinology and metabolism

Tyson H. Holmes

Division of Biostatistics, Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California 94305-5405

A simple framework is introduced that defines ten categories of statistical errors on the basis of type of error, bias or imprecision, and source: sampling, measurement, estimation, hypothesis testing, and reporting. Each of these ten categories is illustrated with examples pertinent to research and publication in the disciplines of endocrinology and metabolism. Some suggested remedies are discussed, where appropriate. A review of recent issues of American Journal of Physiology: Endocrinology and Metabolism and of Endocrinology finds that very small sample sizes may be the most prevalent cause of statistical error in this literature.

statistics; bias; precision; sampling; hypothesis testing



Address for reprint requests and other correspondence: T. H. Holmes, Stanford Univ. School of Medicine, Dept. of Health Research and Policy, Division of Biostatistics, HRP Redwood Bldg., Rm. T160C, Stanford CA 94305-5405 (E-mail: tholmes{at}stanford.edu).




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