Stratified random sampling is useful for populations that are divided into natural subsets.

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Stratified random sampling involves dividing the population into subsets based on certain criteria, and then randomly selecting samples from within each subset. This method is particularly useful when there are important subgroups within the population that need to be represented in the sample, and when there is variability within each subgroup that needs to be accounted for. Thus, stratified random sampling is most effective when working with populations that are divided into natural subsets.

Stratified Random Sampling Is Useful for Populations That Are Divided