What is cluster sampling?

Cluster sampling is a statistical sampling technique that involves dividing a larger population into smaller groups, or clusters, and then randomly selecting some of these clusters for inclusion in the sample. This approach is particularly useful when the population is too large and spread out, making it difficult or costly to conduct a simple random sample of every individual.

By selecting entire clusters, researchers can efficiently gather data from a representative subset while minimizing costs and logistical challenges. Each selected cluster can then be studied in detail, which often leads to practical insights that reflect the larger population. This method is beneficial in various fields, including social sciences and public health, as it allows for flexibility in sample size and accessibility.

The other options do not accurately define cluster sampling. Randomly selecting individuals refers to simple random sampling, convenience sampling focuses on ease rather than representative sampling, and selection based on a specific individual does not align with the concept of sampling from clusters at all.