Stratified random sampling divides the population into:

Stratified random sampling is a method used in statistical research to ensure that various subgroups within a population are represented in a sample. By dividing the population into subgroups, or strata, this technique allows researchers to gather more precise and relevant data, particularly when different subgroups may have distinct characteristics that affect the research outcomes.

Choosing subgroups as the basis for sampling ensures that the sample mirrors the population structure. For instance, if a population consists of various demographic factors such as age, gender, or education level, stratified sampling would involve categorizing individuals based on these demographics before randomly selecting participants from each subgroup. This can help in achieving greater accuracy in representing the entire population in the final analysis.

While the other options might represent ways to group individuals or subsets of data, they do not encompass the full intent and methodology of stratified sampling, which specifically emphasizes the importance of identifying subgroups to enhance the reliability of the results.