In statistics, sampling error or estimation error is the error caused by observing a sample instead of the whole population^[1].

An estimate of a quantity of interest, such as an average or percentage, will generally be subject to sample-to-sample variation.^[1] These variations in the possible sample values of a statistic can theoretically be expressed as sampling errors, although in practice the exact sampling error is typically unknown. Sampling error also refers more broadly to this phenomenon of random sampling variation.

The likely size of the sampling error can generally be controlled by taking a large enough random sample from the population,^[2] although the cost of doing this may be prohibitive; see sample size and statistical power for more detail. If the observations are collected from a random sample, statistical theory provides probabilistic estimates of the likely size of the sampling error for a particular statistic or estimator. These are often expressed in terms of its standard error.

Sampling error can be contrasted with non-sampling error. Non-sampling error is a catch-all term for the deviations from the true value that are not a function of the sample chosen, including various systematic errors and any random errors that are not due to sampling. Non-sampling errors are much harder to quantify than sampling error.^[2]

See also

• Margin of error
• Propagation of error
• Sampling (statistics)

References

External links

• NIST: Selecting Sample Sizes

es:Error muestral pt:Erro amostral