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