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Stratified sampling is ideal when a population includes distinct subgroups (or strata) that the analyst wants to represent proportionally. In this approach, the population is divided into strata based on shared characteristics, such as age, gender, or location. Samples are then randomly selected from each stratum in proportions that reflect the overall population composition. This method reduces sampling error and ensures that each subgroup is represented, which is critical when the characteristics of each subgroup may influence the research results, making stratified sampling especially valuable for studies aiming for diversity or representativeness. The other options are incorrect because: • Option 1 (simple random sampling) does not guarantee subgroup representation, as it draws randomly without stratification. • Option 2 (systematic sampling) may miss certain subgroups if not structured to account for strata. • Option 4 (cluster sampling) is more suited for geographic-based groupings rather than specific characteristics. • Option 5 (convenience sampling) lacks randomness and does not ensure any subgroup proportionality.
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