What does 'stratified sampling' primarily focus on in selecting samples?

Stratified sampling is a method that aims to enhance the representativeness of a sample by ensuring that significant subgroups within a population are adequately included. This technique involves dividing the population into distinct strata or segments, based on specific characteristics relevant to the study, such as age, gender, income level, or any other demographic variables. After creating these strata, researchers then randomly select samples from each subgroup in proportion to their prevalence in the overall population.

By focusing on the inclusion of major subgroups, stratified sampling helps to reduce sampling bias and increases the likelihood that the sample accurately reflects the diversity of the population. This method is particularly useful in research where specific differences among subgroups are crucial to the analysis, allowing for more precise conclusions that are representative of the entire population.

The other options do not capture the essence of stratified sampling; convenience sampling is susceptible to bias as it does not ensure diversity, volunteer sampling may exclude individuals less likely to participate, and random sampling without considering subgroups may overlook important differences within the population.