sample and sampling
sample and samplinga selection of individuals made from a larger population (the parent population) and intended to reflect this population's characteristics in all significant respects. The purpose of taking a sample is to investigate features of the population in greater detail than could be done if the total population was used, and to draw inferences about this population. For these inferences to be valid (see VALIDITY) the sample must be truly representative, the only way to ensure this being to take a RANDOM SAMPLE. This involves using either random numbers or systematic sampling. Random numbers are used to ensure that every individual in the SAMPLING FRAME (e.g. an electoral register or mailing list) has an equal chance of being selected as a member of the sample. Systematic sampling involves randomly selecting the first individual from the list, then subsequently individuals at every fixed interval, e.g. every tenth person if a 10% sample is desired.
When the population to be studied is large and the sample relatively small, it may be efficient to use STRATIFIED SAMPLING. This technique involves dividing the population into strata, e.g. age groups, social classes, and drawing a random sample from each. This can improve the representativeness of the sample, since the size of the sample from each strata is made proportionate to the size of the strata in the total population. See also SAMPLING ERROR, CLUSTER SAMPLING, QUOTA SAMPLING, SNOWBALL SAMPLING, PROBABILITY.