Cluster sampling is classified as what type of sampling method?

Cluster sampling is classified as a probability sampling method because it involves dividing the population into distinct groups, or clusters, and then randomly selecting entire clusters to represent the population. This approach ensures that every individual within those chosen clusters has a known chance of being included in the sample, which is a fundamental principle of probability sampling.

In a probability sampling method, all members of the population have an equal and non-zero chance of being selected, allowing for the statistical validity of the findings and generalizations to the broader population. Cluster sampling is particularly useful when it is impractical or costly to perform a simple random sample over a widely spread population, as it reduces the cost and time involved in data collection.

Considering the context of the other options, a non-probability sampling method would mean that not all individuals have a known chance of being selected, which does not apply here. A descriptive sampling method generally refers to descriptive statistics rather than a specific method of sampling, making it less relevant to this context. A qualitative sampling method often focuses on exploring phenomena rather than generalizing findings, thus differing from the objectives of cluster sampling, which aims at making statistical inferences.