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| '''Statistical bias''' is a feature of a ] technique or of its results whereby the ] of the results differs from the true underlying quantitative ] being ]. | |||
| ==Types== | |||
| A ] is '''biased''' if it is calculated in such a way that it is systematically different from the ] of interest. The following lists some types of biases, which can overlap. | |||
| *] involves individuals being more likely to be selected for study than others, ]. This can also be termed ''Berksonian bias''.<ref>Rothman, K.J. ''et al.'' (2008) ''Modern Epidemiology'' (]) pp.134-137.</ref> | |||
| **] arises from evaluating diagnostic tests on biased patient samples, leading to an overestimate of the ] of the test. | |||
| * The ] is the difference between an estimator's expectations and the true value of the parameter being estimated. | |||
| ** ] is the bias that appears in estimates of parameters in a regression analysis when the assumed specification omits an independent variable that should be in the model. | |||
| * In ], a test is said to be '''unbiased''' when the probability of committing a ] (i.e. false positive) is equal to the significance level. | |||
| * Detection bias occurs when a phenomenon is more likely to be observed for a particular set of study subjects. For instance, the ] involving ] and ] may mean doctors are more likely to look for diabetes in obese patients than in thinner patients, leading to an inflation in diabetes among obese patients because of skewed detection efforts. | |||
| * In ], bias is defined as "Systematic errors in test content, test administration, and/or scoring procedures that can cause some test takers to get either lower or higher scores than their true ability would merit. The source of the bias is irrelevant to the trait the test is intended to measure." <ref>National Council on Measurement in Education http://www.ncme.org/ncme/NCME/Resource_Center/Glossary/NCME/Resource_Center/Glossary1.aspx?hkey=4bb87415-44dc-4088-9ed9-e8515326a061#anchorB</ref> | |||
| * ] may lead to selection of outcomes, test samples, or test procedures that favor a study's financial sponsor. | |||
| * ] involves a skew in the availability of data, such that observations of a certain kind are more likely to be reported. | |||
| * ] arise due to the way that the results are evaluated. | |||
| * ] arise due to the systematic exclusion of certain individuals from the study. | |||
| * ] arises due to a loss of participants e.g. loss to follow up during a study.<ref>{{cite book|last1=Higgins|first1=Julian PT|last2=Green|first2=Sally|title=Cochrane Handbook for Systematic Reviews of Interventions|date=March 2011|publisher=The Cochrane Collaboration|url=http://handbook.cochrane.org/chapter_8/8_4_introduction_to_sources_of_bias_in_clinical_trials.htm}}</ref> | |||
| * ] arises due to differences in the accuracy or completeness of participant recollections of past events. e.g. a patient cannot recall how many cigarettes they smoked last week exactly, leading to over-estimation or under-estimation. | |||
| * ] arises when the researcher subconsciously influences the experiment due to ] where judgement may alter how an experiment is carried out / how results are recorded. | |||
| ==See also== | |||
| * ] | |||
| * ] | |||
| ==References== | |||
| {{Reflist}} | |||
| {{Biases}} | |||
| {{DEFAULTSORT:Bias (Statistics)}} | |||
| ] | |||
| ] | |||
Revision as of 18:32, 11 January 2017
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