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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.
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.
An example of how observer bias can impact on research, and how blinded protocols can impact, can be seen in the trial for an anti-psychotic drug. Researchers that know which of the subjects received the placebo and those that received the trial drugs may later report that the group that received the trial drugs had a calmer disposition, due to ...
In this sense, errors occurring in the process of gathering the sample or cohort cause sampling bias, while errors in any process thereafter cause selection bias. Examples of sampling bias include self-selection, pre-screening of trial participants, discounting trial subjects/tests that did not run to completion and migration bias by excluding ...
In addition to the main result, Ioannidis lists six corollaries for factors that can influence the reliability of published research. Research findings in a scientific field are less likely to be true, the smaller the studies conducted. the smaller the effect sizes. the greater the number and the lesser the selection of tested relationships.
Bias is a distinct concept from consistency: consistent estimators converge in probability to the true value of the parameter, but may be biased or unbiased (see bias versus consistency for more). All else being equal, an unbiased estimator is preferable to a biased estimator, although in practice, biased estimators (with generally small bias ...
For example, confirmation bias produces systematic errors in scientific research based on inductive reasoning (the gradual accumulation of supportive evidence). Similarly, a police detective may identify a suspect early in an investigation, but then may only seek confirming rather than disconfirming evidence.
"You cannot legitimately test a hypothesis on the same data that first suggested that hypothesis. The remedy is clear. Once you have a hypothesis, design a study to search specifically for the effect you now think is there. If the result of this test is statistically significant, you have real evidence at last." [26]