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Stratified sampling is a technique used in statistical sampling that ensures specific subgroups within a population are represented in the sample. The primary advantage of this method is that it guarantees the inclusion of all demographic groups, leading to a more representative sample. By dividing the population into strata based on characteristics such as age, gender, income level, or other relevant factors, researchers can ensure that each subgroup is adequately represented in the final sample. This is particularly important in studies where the objective is to understand the perspectives or behaviors of different segments of the population, as it can lead to more valid and reliable conclusions.

In contrast, other options may reflect aspects of sampling, but they do not capture the distinctive advantage of stratified sampling as effectively. For instance, while stratified sampling can sometimes lead to quicker data collection compared to other methods, it is not inherently less expensive. Additionally, stratified sampling does not focus on gathering data from a homogeneous population; rather, it aims to capture diverse perspectives within a heterogeneous population. Lastly, while it can provide clearer data analysis by ensuring representation, the primary strength of the method lies in its systematic approach to including different demographic groups, not necessarily in simplifying complexity.