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Almost all studies are prone to error—they use samples drawn from a population to estimate what is occurring or what might occur in the whole population. These errors can broadly be divided into two: random error and systematic error. Random error is the play of chance and results in an estimate of effect (for example, relative risk) being equally likely to be above or below the true value. Its role is assessed with statistical measures such as p values and confidence intervals. Systematic error is called bias, and also leads to the estimate being above or below the true value. Systematic error can be further divided into information bias, which relates to the misclassification of data, and selection bias, which is the focus of this article.
Any trend in the collection, analysis, interpretation, publication or review of data that can lead to conclusions that are systematically different from the truth. (Last J. A dictionary of epidemiology, 2001)
Bias limits the conclusions that can be drawn from an analysis. It is particularly problematic because, unlike confounding, little can be done to “allow” or “control” for it once the data have been collected. As such it is in many ways an issue of study design, planning and practice.
Selection bias in epidemiological studies occurs when there is a systematic difference between the characteristics of those selected for the study and those who are not. It also occurs in intervention studies when there are systematic …
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