How is stratified random sampling conducted?

Stratified random sampling is a technique used to ensure that specific subgroups within a population are adequately represented in a sample. This method starts by dividing the larger population into distinct categories or strata that share similar characteristics, such as age, income level, or education. After forming these strata, random samples are drawn from each category.

The key advantage of this approach is that it allows researchers to capture the diversity of the entire population while also ensuring that the individual characteristics of the subgroups are maintained. By applying random sampling within each stratum, this technique reduces sampling bias and enhances the precision of estimates, enabling more accurate conclusions about the overall population. Thus, option B correctly describes the process by emphasizing both the division into categories and the subsequent random selection from each category.