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In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population have a lower or higher sampling probability than others. It results in a biased sample [ 1 ] of a population (or non-human factors) in which all individuals, or instances, were not equally likely to have been selected ...
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] It is sometimes referred to as the selection effect.
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.
Common source bias is a type of sampling bias, occurring when both dependent and independent variables are collected from the same group of people. This bias can occur in various forms of research, such as surveys , experiments , and observational studies . [ 1 ]
In statistics, self-selection bias arises in any situation in which individuals select themselves into a group, causing a biased sample with nonprobability sampling.It is commonly used to describe situations where the characteristics of the people which cause them to select themselves in the group create abnormal or undesirable conditions in the group.
This type of sampling is common in non-probability market research surveys. Convenience Samples: The sample is composed of whatever persons can be most easily accessed to fill out the survey. In non-probability samples the relationship between the target population and the survey sample is immeasurable and potential bias is unknowable.
Overmatching, or post-treatment bias, is matching for an apparent mediator that actually is a result of the exposure. [12] If the mediator itself is stratified, an obscured relation of the exposure to the disease would highly be likely to be induced. [13] Overmatching thus causes statistical bias. [13]
Participation bias or non-response bias is a phenomenon in which the results of studies, polls, etc. become non-representative because the participants disproportionately possess certain traits which affect the outcome. These traits mean the sample is systematically different from the target population, potentially resulting in biased estimates.