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Depending on the type of bias present, researchers and analysts can take different steps to reduce bias on a data set. All types of bias mentioned above have corresponding measures which can be taken to reduce or eliminate their impacts. Bias should be accounted for at every step of the data collection process, beginning with clearly defined ...
If attempts are made to purchase or commission health services using outcomes data, bias may be introduced that will negate the benefits, especially in the service provider produces the outcomes measurement. See Goodhart's Law; Inadequate attention may be paid to the analysis of context data, such as case mix, leading to dubious conclusions. [27]
The field of statistics, where the interpretation of measurements plays a central role, prefers to use the terms bias and variability instead of accuracy and precision: bias is the amount of inaccuracy and variability is the amount of imprecision. A measurement system can be accurate but not precise, precise but not accurate, neither, or both.
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.
In statistics, verification bias is a type of measurement bias in which the results of a diagnostic test affect whether the gold standard procedure is used to verify the test result. This type of bias is also known as "work-up bias" or "referral bias".
Sampling bias is problematic because it is possible that a statistic computed of the sample is systematically erroneous. Sampling bias can lead to a systematic over- or under-estimation of the corresponding parameter in the population. Sampling bias occurs in practice as it is practically impossible to ensure perfect randomness in sampling.
Information bias is also referred to as observational bias and misclassification. A Dictionary of Epidemiology , sponsored by the International Epidemiological Association , defines this as the following:
Another motivation for this form of sensitivity analysis occurs after the experiment was conducted, and the data analysis shows a bias in the estimate of g. Examining the change in g that could result from biases in the several input parameters , that is, the measured quantities, can lead to insight into what caused the bias in the estimate of g .