What is a "stratified sample"?

A stratified sample is designed to ensure that different subgroups within a population are adequately represented. This method involves dividing the population into distinct strata or groups based on specific characteristics, such as age, gender, income level, or other relevant factors. Researchers then take samples from each of these strata in proportion to their size relative to the entire population. This approach enhances the accuracy and generalizability of the results, as it minimizes the chances of overlooking the perspectives or characteristics of smaller or less dominant groups within the population.

Other options, while relevant to sampling methods or statistical terms, do not define a stratified sample. The second choice refers more to random sampling, where each individual has an equal opportunity to be selected without considering group representation. The third choice describes the mean, which is a measure of central tendency, rather than a sampling method. The last option refers to the mode, or the most common observation in a dataset, which again does not pertain to how samples are collected.