Simple random samples and stratified random samples are both statistical measurement tools. A simple random sample is used to represent the entire data population. A stratified random sample divides the population into smaller groups, or strata, based on shared characteristics. However, stratified sampling is more complicated, time-consuming, and potentially more expensive to carry out than simplified random sampling.Stratified random sampling ensures that each subgroup of a given population is adequately represented within the whole sample population of a research study. Stratification can be proportionate or disproportionate. With proportionate stratification, the sample size of each stratum is proportionate to the population size of the stratum. This type of stratified random sampling is often a more precise metric because it's a better representation of the overall population