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| | In the theory of finite [[population]] [[sampling (statistics)|sampling]], a '''sampling design''' specifies for every possible [[sample (statistics)|sample]] its [[probability]] of being drawn. |
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| | [[Mathematics|Mathematically]], a sampling design is denoted by the function <math>P(S)</math> which gives the probability of drawing a sample <math>S.</math> |
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| | == An example of a sampling design == |
| | During [[Bernoulli sampling]], <math>P(S)</math> is given by |
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| | :<math> P(S) = q^{N_\text{sample}(S)} \times (1-q)^{(N_\text{pop} - N_\text{sample}(S))} </math> |
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| | where for each element <math>q</math> is the probability of being included in the sample and <math>N_\text{sample}(S)</math> is the total number of elements in the sample <math>S</math> and <math>N_\text{pop}</math> is the total number of elements in the population (before sampling commenced). |
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| | == See also == |
| | * [[Statistical sampling]] |
| | * [[Bernoulli sampling]] |
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| | == Further reading == |
| | * Sarndal, Swenson, and Wretman (1992), Model Assisted Survey Sampling, Springer-Verlag, ISBN 0-387-40620-4 |
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| | [[Category:Sampling (statistics)]] |
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| | {{statistics-stub}} |
Revision as of 10:37, 8 January 2014
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
An example of a sampling design
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).
See also
Further reading
- Sarndal, Swenson, and Wretman (1992), Model Assisted Survey Sampling, Springer-Verlag, ISBN 0-387-40620-4
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