If an analyst divides the population into groups and randomly selects groups to create the sample, what sampling technique is being used?

The sampling technique being described is cluster sampling. This method involves dividing the entire population into separate groups, or clusters, and then randomly selecting some of these clusters to form a sample. Each cluster is meant to be a mini-representation of the whole population.

In cluster sampling, the selected clusters are used for analysis, and all or a random sample of the elements within those clusters are then observed or measured. This approach is particularly useful in situations where a complete list of the population is not available, but it is feasible to organize the population into identifiable clusters.

The other options present different methodologies: systematic sampling involves selecting every nth individual from a list, stratified sampling requires dividing the population into strata based on shared characteristics and sampling proportionally from each stratum, and simple random sampling means every individual has an equal chance of being selected, without grouping. Therefore, the unique aspect of cluster sampling, which involves selecting whole groups at once, makes it the correct choice in this scenario.