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Concept
Sampling design
Summary
In the theory of finite population sampling, a sampling design specifies for every possible sample its probability of being drawn.
Mathematically, a sampling design is denoted by the function which gives the probability of drawing a sample
During Bernoulli sampling, is given by
where for each element is the probability of being included in the sample and is the total number of elements in the sample and is the total number of elements in the population (before sampling commenced).
In business research, companies must often generate samples of customers, clients, employees, and so forth to gather their opinions. Sample design is also a critical component of marketing research and employee research for many organizations. During sample design, firms must answer questions such as:
What is the relevant population, sampling frame, and sampling unit?
What is the appropriate margin of error that should be achieved?
How should sampling error and non-sampling error be assessed and balanced?
These issues require very careful consideration, and good commentaries are provided in several sources.
Official source
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Sampling (statistics)
In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population. Sampling has lower costs and faster data collection compared to recording data from the entire population, and thus, it can provide insights in cases where it is infeasible to measure an entire population.