Researchers use stratified random sampling to obtain asample population that most appropriately depicts the population being studied.
Stratified sampling has its advantages and disadvantages. For example, itminimizes selection bias and ensures that subgroups within the population receivegenuine representation within the data, but it also includes many conditionsthat must be met in order to be used correctly. Some of the conditions includethat every member of a population must be studied and categorize eachindividual into a subpopulation. Finding an all-inclusive list is just one ofmany problems. Another is correctly categorizing each member of the populationinto a single class. Although this may seem fairly simple with definitive examplessuch as, male and female, however, this become much more challenging when youfactor in race, ethnicity, religion etc.
This selection process becomeincreasingly difficult, showing that this is an inadequate method.