Question

when is cluster sampling most useful?
a. when the population is large and spread out over a wide area
b. when the population is small and easily accessible
c. when every member of the population can be easily listed
d. when detailed information about every member of the population is available

Explanation:

Cluster sampling involves dividing the population into clusters and then randomly selecting some of these clusters. When the population is large and spread out, this method is efficient as it reduces the cost and complexity of sampling compared to other methods like simple random sampling (which would be difficult if the population is widely dispersed). For a small and easily - accessible population (option b), simple random sampling might be more straightforward. If every member can be easily listed (option c) or detailed information about every member is available (option d), other sampling methods like stratified sampling (if there are subgroups) or simple random sampling could be more appropriate. Cluster sampling shines when dealing with a large, dispersed population.

Answer:

A. When the population is large and spread out over a wide area