 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, for 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. The ascertainment bias has basically the same definition, but is still sometimes classified as a separate type of bias. Sampling bias 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 universal be accepted, of sampling bias is that it undermines the external validity of a test. The ability of its results to be generalized to the entire population while selection bias mainly addresses internal validity for differences or similarities found in the sample at hand. In this sense, errors occur in the process of gathering the sample or cohort cause sampling bias, while errors in any process thereafter cause selection bias. However, selection bias and sampling bias are often used synonymously.