Population divided into clusters and a cluster is chosen at random.

• A. Systematic random sampling
• B. Stratified random sampling
• C. Quota sampling
• D. Cluster sampling

Answer: (D)

Cluster sampling works by grouping the population into naturally occurring groups, called clusters, and then randomly selecting whole clusters to study. In this case, the population is divided into clusters and one cluster is chosen at random, which is exactly the defining idea of cluster sampling. If you collect data from every member of that chosen cluster, you have a one-stage cluster sample; if you take a sample within the selected cluster, it would be a two-stage version, but the core idea remains sampling by groups rather than across the whole population.

This approach is often used to save time and resources when the population is large or spread out, since you can focus data collection on a small number of clusters instead of the entire population.

For contrast: systematic random sampling involves picking every k-th individual from a list after a random start, not selecting entire groups. Stratified random sampling divides the population into homogeneous subgroups and samples from each subgroup to ensure representation, rather than randomly selecting entire clusters. Quota sampling is non-probability-based and fills predefined quotas, not based on random cluster selection.