What is one benefit of using stratified random sampling?

Choosing stratified random sampling as a method offers significant advantages, particularly in its ability to control for variability among subgroups. In this approach, the population is divided into distinct subgroups, known as strata, that share specific characteristics relevant to the research. By ensuring that each stratum is adequately represented in the sample, stratified random sampling effectively captures the diversity within the population. This leads to more precise and reliable estimates for the entire population because it accounts for variations that might occur across different segments.

For instance, if a researcher is examining consumer preferences across different income levels, stratified sampling guarantees that each income group is represented proportionately in the sample. This minimizes the risk of variability that could skew the results if certain groups were underrepresented or omitted altogether. Therefore, the main advantage of this method lies in its ability to enhance the accuracy of the data collected by acknowledging and incorporating the nuances of various subgroups within the population.