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Explanation: Stratified sampling divides the population into distinct subgroups or strata based on shared characteristics, such as age, income, or geography. A random sample is then taken from each stratum, ensuring that each subgroup is adequately represented in the sample. This method improves the accuracy of analysis, particularly when the population is heterogeneous, by reducing sampling error and increasing statistical efficiency. Stratified sampling is widely used in surveys and studies where subgroup representation is critical. Option A: While stratified sampling optimizes sampling, it does not inherently minimize the sample size but rather focuses on balanced representation. Option C: Bias can be reduced but not completely eliminated, even in stratified sampling, due to potential flaws in subgroup identification. Option D: Selection in stratified sampling is not based on discretion but on random sampling within strata. Option E: Fixed intervals are a characteristic of systematic sampling, not stratified sampling.
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