Cluster sampling

A resourceful approach to surveying vast populations, cluster sampling is a technique that selects a representative sample by first dividing the population into distinct, non-overlapping groups, or clusters, based on their natural organization or geographical location, and then randomly selecting entire clusters to study, simplifying the data collection process and making large-scale analysis more manageable.

Example

A financial institution conducting a survey on customer satisfaction across its numerous branches might employ cluster sampling to streamline the data collection process. They would first divide their branches into clusters based on geographical regions, such as states or cities. Then, they would randomly select a few clusters to include in their study and survey all customers within those selected clusters. This method helps to efficiently gather insights while maintaining a representative sample, enabling the institution to make informed decisions to improve customer experience and service quality.