# Biased sample

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In statistics sampling bias is causing some members of the population to be less likely to be included than others. It results in a biased sample, a non-random sample[1] of a population (or non-human factors) in which all participants are not equally balanced or objectively represented.[2] If the bias makes estimation of population parameters impossible, the sample is a non-probability sample. If this is not accounted for, results can be erroneously attributed to the phenomenon under study rather than to the method of sampling.

It is a form of sampling error, that is, an error caused by observing a sample instead of the whole population.[3] However, sampling error also includes non-systematic errors that can be decreased by increasing sample size.

It is also called ascertainment bias.[4][5] Ascertainment bias has basically the same definition,[6][7] but is still sometimes classified as a separate type of bias.[6]

## Contents

### Distinction from selection bias

Sampling bias is mostly classified as a subtype of selection bias,[8] sometimes specifically termed sample selection bias,[9][10] but some classify it as a separate type of bias.[11] 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 bias mainly addresses internal validity for differences or similarities found in the sample at hand. 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.