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These differences are interpreted as a kind of bias. Mathematically, the spectrum bias is a sampling bias and not a traditional statistical bias; this has led some authors to refer to the phenomenon as spectrum effects, [3] whilst others maintain it is a bias if the true performance of the test differs from that which is 'expected'. [2]
Spectrum bias arises from evaluating diagnostic tests on biased patient samples, leading to an overestimate of the sensitivity and specificity of the test. For example, a high prevalence of disease in a study population increases positive predictive values, which will cause a bias between the prediction values and the real ones.
Publication bias is a type of bias with regard to what academic research is likely to be published because of a tendency among researchers and journal editors to prefer some outcomes rather than others (e.g., results showing a significant finding), which leads to a problematic bias in the published literature. [138]
Selection bias is the bias introduced by the selection of individuals, groups, or data for analysis in such a way that proper randomization is not achieved, thereby failing to ensure that the sample obtained is representative of the population intended to be analyzed. [1]
Academic bias is the bias or perceived bias of scholars allowing their beliefs to shape their research and the scientific community. It can refer to several types of scholastic prejudice, e.g., logocentrism , phonocentrism , [ 1 ] ethnocentrism or the belief that some sciences and disciplines rank higher than others.
To complement blind or masked protocols and research, further strategies including standardised training for observers and researchers about how to record findings can be useful in the mitigation of observer bias. [1] Clear definition of methodology, tools and the time frames allocated for the collection of findings can assist in adequately ...
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In statistics, the bias of an estimator (or bias function) is the difference between this estimator's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called unbiased. In statistics, "bias" is an objective property of an estimator.