In short, it ensures each subgroup within the population receives proper representation within the sample. As a result, stratified random sampling provides better coverage of the population since the researchers have control over the subgroups to ensure all of them are represented in the sampling.

What is the purpose of stratified sampling?

Stratified random sampling allows researchers to obtain a sample population that best represents the entire population being studied. Stratified random sampling involves dividing the entire population into homogeneous groups called strata.

Why do we stratify data?

Stratification refers to dividing a population or Inference Space up into sub-groups or subunits prior to sampling. ... Because variability is minimized within strata, stratification improves the precision of estimates and is a more efficient sampling technique than simple random selection.