Sampling design

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In the theory of finite population sampling, a sampling design specifies for every possible sample its probability of being drawn.

Mathematical formulation[edit]

Mathematically, a sampling design is denoted by the function P(S) which gives the probability of drawing a sample S.

An example of a sampling design[edit]

During Bernoulli sampling, P(S) is given by

 P(S) = q^{N_\text{sample}(S)} \times (1-q)^{(N_\text{pop} - N_\text{sample}(S))}

where for each element q is the probability of being included in the sample and N_\text{sample}(S) is the total number of elements in the sample S and N_\text{pop} is the total number of elements in the population (before sampling commenced).

Sample design for managerial research[edit]

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 careful consideration, and good commentaries are provided in several sources.[1][2][3]

See also[edit]

Further reading[edit]

  • Sarndal, Swenson, and Wretman (1992), Model Assisted Survey Sampling, Springer-Verlag, ISBN 0-387-40620-4


  1. ^ Mittal, Vikas, Sample Design for Customer-Focused Research (July 30, 2015). Available at SSRN:
  2. ^ Salant, Priscilla, I. Dillman, and A. Don. How to conduct your own survey. No. 300.723 S3.. 1994.
  3. ^ Hansen, Morris H., William N. Hurwitz, and William G. Madow. "Sample Survey Methods and Theory." (1953).