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Explanation: Stratified sampling is the most appropriate technique when a population is divided into distinct subgroups (strata) that need to be proportionally represented in the sample. Each subgroup is sampled independently to maintain its proportion in the overall sample, ensuring a more accurate reflection of population diversity. This method is particularly effective in heterogeneous populations where differences across subgroups significantly affect the variable of interest, such as demographic or geographic studies. It reduces sampling error within subgroups, improving the quality and reliability of data analysis. Option A: Simple random sampling is ineffective for subgroup representation since it treats the entire population as a single homogeneous group. Option C: Cluster sampling groups populations into clusters, selecting entire clusters randomly. It does not guarantee representation from each subgroup, leading to potential bias. Option D: Systematic sampling selects samples at regular intervals, which could inadvertently skip or overrepresent certain subgroups. Option E: Convenience sampling is prone to bias and lacks statistical validity due to non-random selection criteria.
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