What is one advantage of stratified random sampling?

Stratified random sampling is particularly advantageous because it allows for greater precision in the results with a smaller sample size compared to other sampling methods. In this approach, the population is divided into distinct subgroups or strata that share similar characteristics. By ensuring that each stratum is adequately represented in the sample, the variability within each subgroup is reduced, leading to more accurate estimates of population parameters. This precision means that researchers can achieve reliable results even when they survey fewer individuals than they would need in a simple random sampling design.

This method is efficient in terms of resource allocation as it focuses on collecting data from different important categories of the population, which often reflects the diversity of the entire population. Therefore, researchers gain a clearer understanding of trends and relationships within the data without requiring a proportional increase in sample size, which is a common necessity in other sampling techniques.