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"1. A flaw in measuring exposure, covariate, or outcome variables that results in different quality (accuracy) of information between comparison groups. The occurrence of information biases may not be independent of the occurrence of selection biases. 2. Bias in an estimate arising from measurement errors." [2]
The term 'racial misclassification' is commonly used in academic research on this topic but can also refer to incorrect assumptions of another's ethnicity, without misclassifying race (e.g., a person can be misclassified as Chinese when they are Japanese while still being perceived as Asian).
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.
In psychology and cognitive science, a memory bias is a cognitive bias that either enhances or impairs the recall of a memory (either the chances that the memory will be recalled at all, or the amount of time it takes for it to be recalled, or both), or that alters the content of a reported memory. There are many types of memory bias, including:
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 ...
Observer bias is one of the types of detection bias and is defined as any kind of systematic divergence from accurate facts during observation and the recording of data and information in studies. [1] The definition can be further expanded upon to include the systematic difference between what is observed due to variation in observers, and what ...
By matching treated units to similar non-treated units, matching enables a comparison of outcomes among treated and non-treated units to estimate the effect of the treatment reducing bias due to confounding. [1] [2] [3] Propensity score matching, an early matching technique, was developed as part of the Rubin causal model, [4] but has been ...
In practice, as in most of statistics, the difficulties and subtleties are associated with modeling the probability distributions effectively—in this case, (= =). The Bayes classifier is a useful benchmark in statistical classification .