In statistics, sampling error is incurred when the statistical characteristics of a population are estimated from a subset, or sample, of that population. Since the sample does not include all members of the population, statistics on the sample, such as means and quantiles, generally differ from parameters on the entire population. For example, if one measures the height of a thousand individuals from a country of one million, the average height of the thousand is typically not the same as the average height of all one million people in the country. Since sampling is typically done to determine the characteristics of a whole population, the difference between the sample and population values is considered a sampling error Exact measurement of sampling error is generally not feasible since the true population values are unknown; however, sampling error can often be estimated by probabilistic modeling of the sample.
Famous quotes containing the word error:
“There exists a black kingdom which the eyes of man avoid because its landscape fails signally to flatter them. This darkness, which he imagines he can dispense with in describing the light, is error with its unknown characteristics.... Error is certainty’s constant companion. Error is the corollary of evidence. And anything said about truth may equally well be said about error: the delusion will be no greater.”
—Louis Aragon (1897–1982)