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the selection of part of a total population of consumers or products whose behaviour or performance can be analysed, in order to make inferences about the behaviour or performance of the total population, without the difficulty and expense of undertaking a complete census of the whole population.

Samples may be chosen randomly, with every consumer or product in the population having an equal chance of being included. Random samples are most commonly used by firms in QUALITY CONTROL where they are used as a basis for selecting products, components or materials for quality testing.

Alternatively, samples may be chosen by dividing up the total population into a number of distinct sub-groups or strata, then selecting a proportionate number of consumers or products from each sub-group since this is quicker and cheaper than random sampling. In MARKETING RESEARCH and opinion polling, quota sampling is usually employed where interviewers select the particular consumers to be interviewed, choosing the numbers of these consumers in proportion to their occurrence in the total population.

Samples may be:

  1. cross-sectional, where sample observations are collected at a particular point in time, for example data on company sales and the incomes of consumers in the current year, embracing a wide range of different income groups, as a basis for investigating the relationship between sales and income;
  2. longitudinal, where sample observations are collected over a number of time periods, for example data on changes in company sales over a number of years and changes in consumer incomes over the same time periods, as a basis for investigating the relationship between sales and income. See STATISTICAL INFERENCES, QUESTIONNAIRE.
Collins Dictionary of Business, 3rd ed. © 2002, 2005 C Pass, B Lowes, A Pendleton, L Chadwick, D O’Reilly and M Afferson
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The 2010 dataset was obtained by sampling soils according to a stratified random sampling scheme so the mean SOC and associated variance for each SLU complex could be estimated using design-based approaches (de Gruijter et al.
You might be wondering, "If stratified random sampling improves the accuracy of inferences, why isn't it done more often?" The primary reason is insufficient understanding of the population proportion for one or more important characteristic.
Because LQAS is based on stratified random sampling, results from lot samples can be combined to obtain a point estimate for the entire population, allowing for a small sample size.
(1996) used computer simulation for comparing the retention of neutral alleles when forming core collections using nonmarker-based random sampling and stratified random sampling strategies versus the M strategy using genetic markers.
Patent tong surveys use a stratified random sampling design with historical reefs (Baylor 1894, Haven et al.
stratified random sampling so that the class distribution in the samples
Sampling stations were selected using a stratified random sampling design.
Stratified random sampling was also used in order to secure equal numbers of males and females in both settings.
Chandra's study was a rigorous clinical trial that attempted to control for potential confounding variables that may have affected the outcomes through such strategies as random assignment to group and stratified random sampling to ensure equal distribution of high-risk infants across the experimental groups.
For proportional sampling we used a stratified random sampling technique where approximately 50% of the patch was sampled using 3 ha sampling units.