In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population are less likely to be included than others. It results in a biased sample, a non-random sample of a population (or non-human factors) in which all individuals, or instances, were not equally likely to have been selected. If this is not accounted for, results can be erroneously attributed to the phenomenon under study rather than to the method of sampling.
Medical sources sometimes refer to sampling bias as ascertainment bias. Ascertainment bias has basically the same definition, but is still sometimes classified as a separate type of bias.
Other articles related to "sampling bias, bias":
... If entire segments of the population are excluded from a sample, then there are no adjustments that can produce estimates that are representative of the entire population ... But if some groups are underrepresented and the degree of underrepresentation can be quantified, then sample weights can correct the bias ...
... Sampling bias is systematic error due to a non-random sample of a population, causing some members of the population to be less likely to be included than others, resulting in ... It is mostly classified as a subtype of selection bias, sometimes specifically termed sample selection bias, but some classify it as a separate type of bias ... A distinction, albeit not universally accepted, of sampling bias 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 ...
Famous quotes containing the word bias:
“The solar system has no anxiety about its reputation, and the credit of truth and honesty is as safe; nor have I any fear that a skeptical bias can be given by leaning hard on the sides of fate, of practical power, or of trade, which the doctrine of Faith cannot down-weigh.”
—Ralph Waldo Emerson (18031882)