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An important variant of the external validity problem deals with selection bias, also known as sampling bias—that is, bias created when studies are conducted on non-representative samples of the intended population. For example, if a clinical trial is conducted on college students, an investigator may wish to know whether the results ...
A 2011 study reported evidence of cultural bias in Wikipedia articles about famous people on both the English and Polish Wikipedias. These biases included those pertaining to the cultures of both the United States and Poland on each of the corresponding-language Wikipedias, as well as a pro-U.S./English-language bias on both of them. [154]
A distinction of sampling bias (albeit not a universally accepted one) is that it undermines the external validity of a test (the ability of its results to be generalized to the rest of the population), while selection bias mainly addresses internal validity for differences or similarities found in the sample at hand. In this sense, errors ...
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 ...
The only bias that should be evident is the bias attributed to the source. Indicate the relative prominence of opposing views. Ensure that the reporting of different views on a subject adequately reflects the relative levels of support for those views and that it does not give a false impression of parity , or give undue weight to a particular ...
Systemic bias: This page outlines real, useful information about systemic bias on Wikipedia. We are aware that we have biases, and we do what we can to combat them. One against many: This page gives advice for dealing with situations where one editor wants to make a change but multiple editors oppose the change.
Selection bias can occur through natural or deliberate processes that cause a loss of outcome data for members of the intervention and control groups that have already been formed. This is known as attrition and it can come about in two ways (Rossi et al., 2004): targets drop out of the intervention or control group cannot be reached or targets ...
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.