What type of sampling selects groups at random?

The correct choice is cluster sampling, which involves dividing a population into distinct groups, or clusters, and then selecting entire clusters at random for inclusion in the sample. This method is particularly useful when the population is large and geographically dispersed, as it allows for a more manageable sample size while still aiming to maintain the diversity of the population.

In cluster sampling, researchers may choose to randomly select clusters based on predefined criteria, such as geographic location or demographic attributes, and then gather data from all individuals within those selected clusters. This approach can enhance the efficiency of data collection and is often less costly compared to other sampling methods.

Other sampling methods mentioned, such as convenience sampling, involve selecting individuals based on their easy accessibility, which does not guarantee randomness. Systematic sampling, on the other hand, involves selecting every nth individual from a list, which also lacks the randomness inherent to cluster sampling. Random sampling involves choosing individuals from the whole population without forming groups, which, while random, does not specifically select groups at random as cluster sampling does.