In which scenario is stratified sampling most suitable?

Explanation: Stratified sampling is ideal when a population is divided into distinct subgroups (strata) with different characteristics. This method ensures that each subgroup is proportionately represented in the sample, making it highly effective for heterogeneous populations. For example, in a survey studying income levels across various demographics, stratified sampling ensures that all income brackets are included proportionately, leading to more precise and reliable insights. It reduces variability in the results compared to simple random sampling. Option A: Stratified sampling is unnecessary for homogeneous populations as simple random sampling suffices. Option C: While stratified sampling is efficient, its primary advantage lies in representation, not speed. Other sampling methods may be quicker. Option D: Sampling size exceeding the population is illogical and does not apply to any valid sampling method. Option E: When only a single characteristic is of interest, simple random sampling is usually adequate. Stratified sampling is designed for analyzing multiple subgroups.