What is cluster sampling?

• A. A method that selects individuals randomly across the entire population.
• B. Divides population into strata and samples from each.
• C. Selects whole groups (clusters) like classrooms, then samples within those clusters.
• D. Uses convenience samples.

Answer: (C)

Cluster sampling groups the population into natural units called clusters, then selects some clusters to study and either surveys everyone in those clusters or samples within them. The option described fits this two-stage idea: you pick whole groups such as classrooms and then take a sample from within those groups. This approach is useful when listing every individual in the population is hard or costly, because working with whole clusters reduces travel and administrative effort. It differs from random sampling across the whole population, which aims for each individual to have an equal chance of selection, and from stratified sampling, which samples from every subgroup to ensure representation. It’s also not a convenience sample, which relies on easily accessible people. A trade-off is that people within the same cluster tend to be more similar to each other, which can increase sampling error if clusters aren’t well chosen or many clusters aren’t included.