What is a defining feature of cluster sampling?

Cluster sampling is characterized by the selection of entire groups or clusters instead of selecting individual participants based on specific characteristics of the population. In this method, the population is divided into clusters, which are often naturally occurring groups such as geographical areas or institutions. A sample of these clusters is then randomly selected, and data is collected from all individuals within those chosen clusters. This approach is particularly practical when the population is large and dispersed, making it more efficient to gather data from clusters rather than conducting a broader individual sampling.

The emphasis on using a single group as a sampling element is what distinguishes cluster sampling from other techniques, such as stratified sampling or simple random sampling, which do not focus on whole groups but rather on individuals or strata that are characteristic of the population as a whole.