What does cluster sampling involve in the selection process?

Cluster sampling is a technique used in statistical sampling where the population is divided into groups, known as clusters, and a sample of these clusters is selected for analysis. Each cluster is typically made up of a naturally occurring or easily identifiable group that represents a segment of the population.

The key characteristic of cluster sampling is that entire clusters are selected for inclusion in the study rather than individual units from the overall population. This method is efficient when the population is widespread or difficult to access, as it allows researchers to gather data from a limited number of clusters, which can significantly reduce costs and time associated with data collection.

Choosing sampling units in convenient groups effectively captures this essence, as it emphasizes the grouping of participants into clusters for selection—the defining feature of this sampling method. By focusing on these clusters, researchers can ensure that the sample is representative of the population while also leveraging logistical advantages.