Which best describes stratified sampling?

Stratified sampling is characterized by dividing the entire population into distinct subgroups, or "strata," based on shared characteristics such as age, gender, income level, or any other relevant attribute. The key aspect of stratified sampling is that it ensures representation from each of these subgroups. Once the population is divided, a specified number of individuals are randomly selected from each stratum. This approach can lead to more accurate and reliable results compared to other sampling methods, particularly when the subgroups differ significantly regarding the outcome being studied.

This method is particularly effective in ensuring that all subgroups are adequately represented, which can minimize sampling bias and enhance the precision of estimates related to the entire population. By focusing on subgroups, researchers can also analyze variation within each stratum, providing deeper insights into the data collected.

The other methods mentioned do not capture the essence of stratified sampling, since they either rely on systematic selection, voluntary response, or fail to account for the diversity within the population. This focus on sampling from each subgroup distinctly defines the strength and applicability of stratified sampling in statistical research.