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Sampling procedures ^{1} are used to obtain information about a population from part of the population only instead of having to study every person (1102). The part of the population studied is called a sample ^{2}. A population is a collection of elements ^{3} which are the object of the investigation. A sampling unit ^{4}may be an element or a group of elements of the population and is used for selecting samples. In demographic samples the elements are generally individuals (1102), families (1121) or households (1103), and sampling units may be individuals, households, blocks of houses, municipalities or areas. The sample will consist a number of sampling units selected in accordance with a sampling scheme ^{5} or sampling plan ^{5}.
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A sample whose elements are selected by a chance process is called a probability sample ^{1}. If a complete list of sampling units is available this is called a sampling frame ^{3}. In simple random sampling ^{4} a proportion of sampling units is selected from the frame at random ^{2}. This proportion is called the sampling fraction ^{5}. Systematic samples ^{6} are taken from a frame in which the sampling units are consecutively numbered. The sample is drawn by taking the n^{th}, (n + s)^{th}, (n + 2s)^{th}..., etc. unit, where n is not larger than s and is selected at random. In cluster sampling ^{7} population elements are not drawn individually, but in groups which are called clusters ^{8}.
 2. random — randomness n. — randomize v.
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In stratified random sampling ^{1} the population is divided into a number of strata ^{2} which are in some sense more homogeneous than the population as a whole with respect to the characteristics studied, and a simple random sample (cf. 1614) is drawn in each stratum. Variable sampling fractions (1615) may be used in different strata. Multistage sampling ^{3} is a method where the selection of the sample is carried out in several stages. A sample of primary sampling units (1604) is first selected and each of these units is then regarded as a population from which a subsample ^{4} is selected, and the process may be repeated. Where there is no good sampling frame, a sample of areas delimited on a map may be selected: this procedure is called area sampling ^{5}.
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In probability sampling (1611), chance methods are used to obtain a representative sample ^{1}, i. e., a sample which is a relatively faithful reflection of the population with respect to the character or characters studied. In quota sampling ^{2}, on the other hand, the sample is purposely selected so as to reflect the population in certain characteristics, and each interviewer (2042) is given a quota ^{3} of different types of sampling units which are to be included in his sample. Within the limits of the quota the interviewer is free to select the sampling units.
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A population parameter ^{1} is a numerical value that characterizes a population. Statistical estimation ^{2} is the name given to the procedure by which the values of such parameters can be estimated from the sample. Such estimates are subject to sampling errors ^{3} and a measure of the magnitude of the sampling error is generally given by the standard error ^{4}. Sometimes a confidence interval ^{5} is associated with an estimate to show the limits within which the estimated quantity may be expected to lie. A difference between two values is called a significant difference ^{6} when the probability that it is due to chance is less than a given value which is called the level of significance ^{7}. Thus a difference will be significant at the 5 per cent level if the probability that it could have arisen by chance is less than 0.05.
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