Which sampling method divides the population into subgroups before sampling?

The correct answer is stratified random sampling because this method involves dividing the overall population into distinct subgroups, known as strata, which share similar characteristics. By categorizing the population into these subgroups—such as age, income, or education level—the researcher can ensure that each subgroup is adequately represented in the sample. After the population has been divided, random samples are drawn from each stratum, enhancing the representativeness of the sample and improving the accuracy of statistical analysis.

This approach is particularly valuable when researchers anticipate that different subgroups may have varying responses or behaviors concerning the research subject, making it essential to capture this diversity. For instance, if a study aims to understand consumer preferences across different age groups, stratified sampling would allow the study to ensure that each age group is proportionally represented in the final sample, leading to more robust and reliable findings.

In contrast, methods like simple random sampling do not involve pre-dividing the population and involve selecting individuals purely at random from the entire population, which may overlook specific subgroup characteristics. Cluster sampling selects entire groups or clusters randomly but does not operate on the characteristic of subgroups within the larger population, while snowball sampling relies on referrals from initial subjects and is often used in hard-to-reach