Which sampling method involves selecting clusters or groups from a population?

The chosen answer is correct because the cluster sampling method involves dividing the entire population into clusters or groups, and then a random selection of these clusters is made. Instead of sampling individuals from the population, this approach allows researchers to study larger groups more efficiently. This can be particularly useful when populations are large or geographically dispersed, as it minimizes travel and research costs.

In contrast, the other sampling methods mentioned operate differently. Random sampling entails selecting individuals randomly from the entire population, ensuring that every individual has an equal chance of being selected, without the use of clustered groups. Systematic sampling follows a predetermined system, such as selecting every nth individual from a list, instead of grouping. Stratified sampling involves dividing the population into distinct subgroups (strata) and sampling from each stratum separately to ensure representation of different segments within the population.

Overall, cluster sampling stands out because it focuses on groups rather than individuals, making it a more efficient method in certain research scenarios.