Which sampling method divides a population into subgroups before sampling?

Stratified sampling is a method that involves dividing a population into distinct subgroups or strata that share similar characteristics. This approach is particularly useful when researchers want to ensure that specific subgroups within the population are adequately represented in the sample. For example, if a population is comprised of different age groups, income levels, or geographic areas, stratified sampling allows for each subgroup to be sampled proportionally.

By first identifying these subgroups, the researcher can then proceed to sample from each stratum, ensuring that the sample reflects the diversity within the population. This method enhances the accuracy and reliability of the results, as it reduces the potential for sampling bias that could occur if the sample were taken randomly without considering the characteristics of the population.

In contrast, other sampling methods mentioned do not prioritize subgroup differentiation in the same way. Cluster sampling, for example, involves dividing the population into clusters and then randomly selecting entire clusters for study, without focusing on the characteristics of individuals within those clusters. Area sampling similarly involves geographic division but may not account for other subgroup characteristics. Random sampling, while effective at ensuring that each individual has an equal chance of selection, does not involve any preliminary division of the population into subgroups. Thus, the distinctive characteristic of stratified sampling is