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The effect(s) of such misclassification can vary from an overestimation to an underestimation of the true value. [4] Statisticians have developed methods to adjust for this type of bias, which may assist somewhat in compensating for this problem when known and when it is quantifiable. [5]
Recall bias is of particular concern in retrospective studies that use a case-control design to investigate the etiology of a disease or psychiatric condition. [ 3 ] [ 4 ] [ 5 ] For example, in studies of risk factors for breast cancer , women who have had the disease may search their memories more thoroughly than members of the unaffected ...
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Selection bias refers to the problem that, at pre-test, differences between groups exist that may interact with the independent variable and thus be 'responsible' for the observed outcome. Researchers and participants bring to the experiment a myriad of characteristics, some learned and others inherent.
For example, the individual components of a differential white blood cell count must all add up to 100, because each is a percentage of the total. Data that is embedded in narrative text (e.g., interview transcripts) must be manually coded into discrete variables that a statistical or machine-learning package can deal with.
Suppose a pair (,) takes values in {,, …,}, where is the class label of an element whose features are given by .Assume that the conditional distribution of X, given that the label Y takes the value r is given by (=) =,, …, where "" means "is distributed as", and where denotes a probability distribution.
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Detection bias occurs when a phenomenon is more likely to be observed 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 thinner patients, leading to an inflation in diabetes among obese patients because of skewed detection efforts.