What is stratified sampling?

Stratified sampling is a statistical technique that involves dividing a population into distinct subgroups, known as strata, based on specific characteristics relevant to the research, such as age, gender, income level, or education. This method ensures that each subgroup is represented proportionately within the overall sample, allowing for more accurate and reliable results regarding the entire population.

By ensuring that these subgroups are accurately represented, stratified sampling enhances the precision of estimates and reduces sampling error. For example, if a study aims to understand the behavior of a population that consists of distinct age groups, researchers can stratify the sample by these age groups. This way, they ensure that each age group is adequately represented in the sample, leading to findings that better reflect the diversity and characteristics of the entire population.

This method is especially beneficial in research where certain subgroups may differ significantly from others regarding the variable of interest. By focusing on these subgroups, researchers can make more nuanced analyses and conclusions that consider the intricacies within different sections of the population.