Pub. date: 2008 | Online Pub. Date: November 27, 2007 | DOI: 10.4135/9781412953948 | Print ISBN: 9781412928168 | Online ISBN: 9781412953948 | Publisher:SAGE Publications, Inc.About this encyclopedia
Louise-Anne McNutt & Allison Krug & Colleen McLaughlin
Error happens. Scales are set incorrectly. Memories fade. Study participants try to give the ‘right’ answer. When these mistakes are systematic, that is, not random, they will likely cause a biased result. The term bias can be translated fairly as ‘wrong’ with the additional refinement of ‘wrong due to systematic error.’ Thus, in the general statistical and scientific languages, a biased estimate is an incorrect estimate. In epidemiology, biased estimates typically refer to the distortion of a measure of association between exposure and outcome. This entry describes these measures, including the rate ratio, relative risk, attributable risk, and odds ratio. The following examples will help illustrate bias. Underestimation would be caused by a scale that always weighs people 10 lb less than their true weight. A survey with leading questions (e.g., ‘Do you believe that smoking is bad?’) may draw the desired answers more frequently than the study population actually ...