Which method involves dividing a population into segments for sampling?

Stratified random sampling is a method where the population is divided into distinct subgroups, or strata, that share similar characteristics. This approach ensures that each segment of the population is appropriately represented in the sample. For example, if a researcher is studying a population divided by age, gender, or income level, they would first categorize the population into these segments. Then, random samples would be taken from each subgroup to ensure that the overall sample reflects the diversity of the entire population.

This method is particularly beneficial when the researchers believe that certain segments are important for the analysis and want to ensure that their findings are not biased towards just one part of the population. By stratifying, the researcher can capture variations across different strata, leading to more reliable and accurate results.