Social Research Glossary   A B   Citation reference: Harvey, L., 2012-20, Social Research Glossary, Quality Research International, http://www.qualityresearchinternational.com/socialresearch/ This is a dynamic glossary and the author would welcome any e-mail suggestions for additions or amendments. Page updated 19 December, 2019 , © Lee Harvey 2012–2020. A fast-paced novel of conjecture and surprises

_________________________________________________________________

Sampling error

core definition

Sampling error is the random variation that comes from sampling and cannot be eliminated but may be estimated.

explanatory context

Sampling from populations leads to problems of inference about population statistics, because samples are not identical to the populations from which they are drawn. They are an approximation. Randomly selected samples are the 'best' approximation because they are not inherently biased. But they are still prone to sampling error. They are not an exact microcosm of the population.

To be representative (and therefore unbiased) a random sample should be selected where the probability that any member of the population being in the sample is known.

Sampling error is the variation that arises from selecting random samples. Samples are extremely unlikely to exactly reflect the population from which they are drawn. There will be some variation between the sample and the population. Any given sample parameter (mean, proportion etc.) will be an estimate of the population parameter. Given random sampling, the estimate is as likely to be an overestimate as an underestimate.

It is unlikely that a sample statistic will vary considerably from the population statistic. Equally, it is unlikely that a sample statistic will be exactly the same as the population statistic.

Two factors determine the extent to which a sample parameter is likely to vary from the population parameter. First, the sample size. The bigger the sample, the less variation there is between the sample parameter and the population parameter. (I.e. the sampling error declines as sample size increases).

Second, the population variation. The less the population varies the less the sample parameter is likely to vary from the population parameter.

analytical review

associated issues

related areas

sampling

significance test

Sources

A NOVEL
Top

A B