Which sampling technique is most appropriate when the population is naturally divided into groups that differ significantly from each other?

Stratified sampling is used when the population is divided into distinct subgroups, or strata, that differ from each other in key characteristics (e.g., age, income, education level). This technique ensures that each subgroup is adequately represented in the sample by selecting samples proportionally from each stratum. For instance, if a researcher wants to study customer satisfaction among various age groups, stratified sampling would ensure that each age group is represented in proportion to its size in the population. This increases the accuracy of the results and helps make more reliable comparisons between subgroups. Why Other Options Are Incorrect: • A: Simple random sampling does not consider subgroups and may not be effective in capturing important differences in a population. • C: Cluster sampling involves dividing the population into groups and then randomly selecting whole groups, which may not capture the diversity within subgroups. • D: Systematic sampling selects every nth individual, which may not provide the representative diversity needed in heterogeneous populations. • E: Convenience sampling is based on easy access to data and does not guarantee that all subgroups will be adequately represented.