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:
“They have their belief, these poor Tibet people, that Providence sends down always an Incarnation of Himself into every generation. At bottom some belief in a kind of pope! At bottom still better, a belief that there is a Greatest Man; that he is discoverable; that, once discovered, we ought to treat him with an obedience which knows no bounds. This is the truth of Grand Lamaism; the “discoverability” is the only error here.”
—Thomas Carlyle (1795–1881)