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.
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thumb|Exemple d'échantillonnage aléatoire En statistique, l'échantillonnage désigne les méthodes de sélection d'un sous-ensemble d'individus (un échantillon) à l'intérieur d'une population pour estimer les caractéristiques de l'ensemble de la population. Cette méthode présente plusieurs avantages : une étude restreinte sur une partie de la population, un moindre coût, une collecte des données plus rapide que si l'étude avait été réalisé sur l'ensemble de la population, la réalisation de contrôles destructifs Les résultats obtenus constituent un échantillon.
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