In stratified sampling, what is primarily done?

In stratified sampling, participants are selected from every subgroup proportionally. This method is used to ensure that different segments of a population are adequately represented in the sample. By dividing the population into distinct subgroups, or strata, such as age, gender, income level, or education, researchers can ensure that each subgroup is reflected in the final sample according to its proportion in the larger population. This proportional representation is crucial for reducing sampling bias and increasing the generalizability of the research findings.

For instance, if a population is composed of 60% females and 40% males, in a stratified sample, the researchers would aim to select 60% female participants and 40% male participants in their sample. This approach increases the accuracy of the results, allowing for more reliable comparisons and conclusions about the different subgroups within the population.