What does stratified random sampling entail?

Stratified random sampling involves first dividing a population into distinct subgroups or strata that share certain characteristics. After identifying these subgroups, random sampling is then conducted within each stratum. This method ensures that each of the identified subgroups is adequately represented in the final sample, which can lead to more accurate and generalizable results.

The approach is particularly beneficial when researchers anticipate that variations within the subgroups could have a significant impact on the overall outcome of the study. By ensuring that different segments of the population are represented, stratified random sampling can help mitigate sampling bias and provide a clearer understanding of the population as a whole.

In contrast, other methods—such as selecting individuals at fixed intervals or from naturally occurring groups—do not account for the diversity within the population in the same systematic way that stratified random sampling does. Additionally, non-random selection based on characteristics does not adhere to the principles of randomness, which are fundamental to the validity of conclusions drawn from statistical analyses. Hence, the correct understanding of stratified random sampling as random selection within identified subgroups is key to comprehensive evidence-informed research.