In cluster sampling, diverse population subgroups are considered clusters, and individuals from each cluster are randomly chosen. Stratified sampling and cluster sampling are distinct from one another. In stratified sampling, the analyst divides the population into different groups based on factors such as age, sex, profession, and so forth, whereas in cluster sampling, researchers randomly choose from pre-existing or naturally occurring groups or clusters, such as towns within a township or families within society. In this strategy, we first create clusters based on our needs, and then researchers choose samples using either methodical or simple random sampling.
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