What does stratified random sampling help to achieve?

Stratified random sampling is designed to ensure that different subgroups or strata within a population are adequately represented in the sample. By dividing the population into distinct strata based on specific characteristics (such as age, gender, income level, etc.) and then randomly selecting samples from each stratum, this approach enhances the representativeness of the sample. As a result, the sample mirrors the diversity of the entire population more effectively, meaning that any insights or data collected from the sample can be generalized with greater confidence to the whole population.

This methodology is particularly valuable in research where certain subgroups may have different behaviors, opinions, or characteristics that are crucial to the study’s objectives. By ensuring that each subgroup is represented according to its proportion in the population, stratified random sampling addresses variances that could bias results in a simple random sample, which might overlook smaller, yet significant, groups. Thus, the correct answer highlights the primary benefit of stratified random sampling in creating a sample that is truly reflective of the larger population.