How does stratified sampling differ from simple random sampling?

Stratified sampling is characterized by its approach of dividing a population into distinct subgroups, or strata, that share similar characteristics. In stratified sampling, researchers ensure that each subgroup is adequately represented in the final sample, which allows for more precise and reliable estimates for the population as a whole. This method is particularly beneficial when the researcher is aware that certain subgroups within the population may behave differently or have different characteristics important to the study.

This is in stark contrast to simple random sampling, which selects individuals from the entire population without considering any subgroup characteristics. Simple random sampling treats the entire population as a uniform entity, which can sometimes lead to underrepresentation of certain subgroups. Therefore, while both sampling methods aim to draw conclusions about populations, stratified sampling is specifically designed to enhance the representation of various subgroups, thereby improving the overall validity and reliability of the study findings.