Bias (statistics)

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A statistic is biased if it is calculated in such a way that is systematically different from the population parameter of interest. The following lists some types of, or aspects of, bias which should not be considered mutually exclusive:

• Selection bias, where individuals or groups are more likely to take part in a research project than others, resulting in biased samples. This can also be termed Berksonian bias.^[1]
  • Spectrum bias arises from evaluating diagnostic tests on biased patient samples, leading to an overestimate of the sensitivity and specificity of the test.
• The bias of an estimator is the difference between an estimator's expectation and the true value of the parameter being estimated.
  • Omitted-variable bias is the bias that appears in estimates of parameters in a regression analysis when the assumed specification is incorrect, in that it omits an independent variable that should be in the model.
• In statistical hypothesis testing, a test is said to be unbiased when the probability of rejecting the null hypothesis is less than or equal to the significance level when the null hypothesis is true, and the probability of rejecting the null hypothesis is greater than or equal to the significance level when the alternative hypothesis is true,
• Detection bias is where a phenomenon is more likely to be observed and/or reported for a particular set of study subjects. For instance, the syndemic involving obesity and diabetes may mean doctors are more likely to look for diabetes in obese patients than in less overweight patients, leading to an inflation in diabetes among obese patients because of skewed detection efforts.
• Funding bias may lead to selection of outcomes, test samples, or test procedures that favor a study's financial sponsor.
• Reporting bias involves a skew in the availability of data, such that observations of a certain kind may be more likely to be reported and consequently used in research.
• Data-snooping bias comes from the misuse of data mining techniques.

v t e Biases Cognitive bias Acquiescence bias Anchoring bias Attentional bias Attributional bias Belief bias Choice-supportive bias Confirmation bias Congruence bias Correspondence bias Halo effect Hindsight bias Introspection illusion Memory bias Motivated reasoning Outcome bias Publication bias Reporting bias Self-serving bias Status quo bias Statistical bias Ascertainment bias Bias of an estimator Information bias Lead time bias Observer bias Omitted-variable bias Recall bias Response bias Sampling bias Selection bias Spectrum bias Systematic bias Systemic bias Other/ungrouped FUTON bias No abstract available bias

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