Why are clusters used in cluster sampling?

• A. To survey the entire population easily
• B. To guarantee equal sampling across subgroups
• C. To reduce costs and increase practicality when a full population listing is not feasible
• D. To ensure randomization is not used

Answer: (C)

Clustering is used to cut costs and increase practicality when a complete listing of every member of the population isn’t feasible. By grouping the population into manageable clusters (like neighborhoods or schools), you can randomly select a subset of clusters and collect data from those clusters. This dramatically reduces travel, time, and administrative effort compared with surveying everyone or creating a full population frame. The trade-off is a potential loss of precision because members within the same cluster tend to resemble each other, but with careful design the approach still yields useful estimates at much lower cost.

Choosing the entire population would require listing and surveying everyone, which cluster sampling is designed to avoid. Aiming for equal sampling across subgroups describes stratified sampling, not cluster sampling. And randomization is typically used to select clusters, so saying randomization isn’t used isn’t accurate.