Error, bias, and confounding in epidemiology

Abstract Epidemiological studies are prone to error, because they usually study complex matters in human populations in natural settings and not in laboratory conditions. Bias may be thought of as error which affects comparison groups unequally or leads to inappropriate inferences about one group compared with another. Three broad problems confront epidemiologists: selection of study populations, quality of information, and confounding. Selection and imperfect information cause biases. Confounding is not an error or bias as normally understood, but it leads to errors of data interpretation. The different epidemiological research designs have similar problems with error and bias, which are mostly inherent in the survey and disease registration methods. Principles which apply to all studies and help to minimize these errors are also similar. The chronology and structure of a research project offers a pragmatic framework for the systematic analysis of error bias and confounding.