Cluster sampling

Cluster sampling is a practical way to study large populations. It works by dividing the population into distinct, non-overlapping groups, called clusters, based on natural structure or geographic location. You then randomly select whole clusters and study them, which makes data collection simpler and easier to manage at scale.

Example

A financial institution running a customer satisfaction survey across many branches might use cluster sampling to simplify data collection. It could first group branches into clusters by geographic region, such as states or cities. Then it would randomly select a few clusters for the study and survey all customers within those clusters. This approach helps the institution gather useful insights while keeping the sample representative, so it can make better decisions about customer experience and service quality.