Researchers
use stratified random sampling to obtain a sample population that best
represents the entire population being studied. Its advantages include
minimizing sample selection bias (a type of bias caused by choosing non-random
data from statistical analysis) and it ensures each subgroup within the
population receives proper representation within the sample. The disadvantages
of stratified sampling include that several conditions must be met for it to be
used properly. Researchers must identify every member of a population being
studied and classify each of them into one, subpopulation. Finding an
exhaustive and definitive list of an entire population is the first challenge.

The other challenge is accurately sorting each member of the population into a
single stratum. This example is fairly simple; undergraduate, graduate, male
and female are clearly defined groups. In other situations, however it is far
more difficult. Imagine bringing defining characteristics such as race,
ethnicity or religion into play. The sorting process becomes more difficult,
rendering stratified random sampling an ineffective method.

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