How does cluster sampling differ from simple random sampling?

Cluster sampling differs from simple random sampling mainly because in cluster sampling, elements are grouped into clusters before the actual sampling occurs. This method involves dividing the entire population into distinct groups or clusters, usually based on geographical or other relevant criteria. Then, a selection of these clusters is randomly chosen, and all individuals within the selected clusters are included in the sample. This approach can be particularly useful when dealing with large populations that are difficult to access in their entirety, as it allows researchers to manage and reduce resources, such as time and costs, associated with sampling.

In contrast, simple random sampling does not involve any grouping or clustering; instead, it selects individuals from the entire population completely at random, ensuring every member has an equal chance of being included, irrespective of any inherent grouping within the population. This fundamental difference in methodology highlights why the correct choice reflects the essence of how cluster sampling operates in comparison to simple random sampling.