Solution

Stratified sampling is ideal for situations where the population can be divided into distinct subgroups, or "strata," each representing a specific characteristic, such as age or income level. In stratified sampling, the population is split into these strata, and random samples are drawn from each to ensure proportional representation. This approach is especially useful when the subgroup characteristics are expected to influence the analysis, as it reduces sampling error and enhances the precision of the results. Option A is incorrect because simple random sampling does not ensure representation of subgroups. Option B is incorrect as systematic sampling selects every nth individual, which does not account for subgroups. Option C is incorrect because cluster sampling involves dividing the population into clusters, but it does not ensure proportional representation within subgroups. Option E is incorrect as convenience sampling lacks structure and does not aim for representativeness.