Stratified random sampling

When we have supplemental information available to aid with the sample design, we can consider using stratified random sampling. As the name suggests, we divide the population into strata or groups based on certain characteristics by which we can identify the groups. Now, we select the elements from these groups to create a sample. This way, we can ensure the representation of the overall population. These subgroups are formed based on attributes like a particular age group, gender, occupation. If your population has a lot of variation, you want to use stratified random sampling.

For instance, suppose the government wants feedback on a new education policy they are going to pursue. It will not be sufficient to survey only the stakeholders of government schools, which might be easier to accomplish. The sample would need representation from all strata on which the policy might have implications like private, semi-private, minority, international schools, in addition to government schools.