Which sampling method relies on pre-existing groups for selection?

Cluster sampling is the correct choice as it specifically involves dividing the population into pre-existing groups, or clusters, and then selecting whole clusters for analysis. This method is particularly useful when a complete list of the population is not available or when it is impractical to conduct a simple random sample.

In cluster sampling, these groups are often formed based on natural divisions within the population, such as geographical areas or demographic categories, which makes this method practical and efficient for researchers. Once the clusters are selected, all individuals within the chosen clusters may be surveyed, maximizing resource use while still aiming for representative data.

In contrast, other sampling methods like stratified sampling require dividing the population into strata based on specific characteristics before sampling occurs, random sampling focuses on selecting individuals purely at random without regard to groups, and systematic sampling involves selecting individuals based on a fixed interval from a list. Each of those methods has a different approach to selection and does not specifically rely on pre-existing group structures in the same manner as cluster sampling.