sampling

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Related to Sample size: sampling techniques

sampling

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.
References in periodicals archive ?
a]), is a function of sample size n, type I error [alpha] and values of [mu] specified in the null [H.
Moreover, this fit statistic does not depend on the sample size or test length.
Thus it was suggested to include a large sample size for the small bias in ACS, as it happened in simple random sampling.
Similar results were reported by PINTO (2009), who observed that the power values of the LRT (for a specific covariance) with exact distribution and of the LRT with approximate chi-squared distribution were similar when the sample size was increased to 50 and 100 observations.
Design of the trial should be clearly mentioned (Superiority trial, non-inferiority trial, equivalence trial) * All components of sample size calculation should be reported.
Thus, the decision about the optimal test sample size for each TC is a complex problem in the flash storage manufacturing industry.
However, it was clear the relationship between sample size (n) with the proportion of null values (p) in the approximation of results from power, considering both parametric values of [theta] assumed.
The second approach is "sampling accounts individually," where sample size is determined by the parameters set for each account separately.
The minimum required sample size using these values is n = 16 in both the test and control groups (12).
Hence if your intervention produces individual responses, the estimate of sample size based on a reliability study will be too low.
In view of the imprecision of parameter settings, Shiffler and Harwood (1985) investigated the effect on the realized [alpha]-risk of using a pilot sample variance to estimate variance on the sample size formula for testing population means.