What sampling technique divides the population into subsets and then selects a random sample within each subset?

Stratified random sampling is a technique that involves dividing the population into distinct subgroups, or strata, that share similar characteristics. The purpose of this approach is to ensure that each subgroup is adequately represented within the overall sample. After the population is divided into these strata, a random sample is selected from each subgroup.

This technique is particularly advantageous when there is reason to believe that the differences between the subgroups might influence the outcome of the study. By sampling from each stratum, researchers can ensure that their findings are more generalizable to the entire population, as they can evaluate the effects within specific groups as well as across the overall population.

For example, if a study is looking at a new health intervention, it might stratify the population by age, gender, or other relevant factors, allowing for more targeted insights that could lead to more effective outcomes.

In contrast, other sampling methods mentioned, like systematic sampling or simple random sampling, do not involve dividing the population into strata, and cluster sampling involves selecting whole groups or clusters rather than individual strata. This makes stratified random sampling the most suitable technique for the question posed.