title: Ten categories of statistical errors: a guide for research in endocrinology and metabolism.
abstract: 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.
authors.affiliation: Stanford University School of Medicine, Department of Health Research and Policy, Division of Biostatistics, HRP Redwood Building, Rm. T160C, Stanford CA 94305-5405, USA. tholmes@stanford.edu
authors.forename: Tyson H
authors.initials: TH
authors.lastname: Holmes
mesh_terms: D015982:Bias; D003627:Data Interpretation, Statistical; D004704:Endocrinology / Q000706:statistics & numerical data*; D008660:Metabolism; D017594:Publication Bias; D015203:Reproducibility of Results; D012107:Research Design; D015983:Selection Bias