The elements of statistics such as survey methodology, quality assurance, and sampling are useful in the selection of population samples for purposes of estimation of the whole population’s characteristics. The observation from research does measure properties such as color, location, and weight of observable objects or individuals that are distinguished as an independent. Weight is usually applied to data in the adjustment of sample design and in particular, stratified sampling. Results from statistical and probability theory are employed in guiding the practice. The process of sampling is widely used in information gathering about a population.
Stratified sampling is a type of sampling in which the population is subdivided into two different parts or groups known as strata ( Barnett , 2002). Each stratum shares common characteristics and a sample is normally drawn from each stratum. An example of this is during research on the burden of disease in a population, the records of the sick are taken and then grouped according to the disease they suffer from. That will then provide the burden of individual diseases. Cluster sampling, on the other hand, divides a population into sections which will then be randomly selected and then all members of those selected sections chosen. For example, when looking for volunteers in a group 50 people, the group could be divided into three sections bearing five people each. Random sampling involves the selection of a population sample in such manner that every member of the population stands equal chances of selection. An example is when the population of fish in a pond is being measured ( Barnett, 2002) . Samples are taken such that each fish stands a chance of representation. Averages will then be computed such that the population is equally represented.
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References
Barnett, V. (2002). Sample survey: Principles and methods . London: Arnold.
