Respuesta :
Answer:
The population can be divided into strata so that the individuals in each stratum are as much alike as possible.
Step-by-step explanation:
In short, if you can group the population into subgroups and are interested in their sub properties, then use stratified random sampling. If not, and interested only in the entire population as a whole, then use simple random sampling.
Simple random sampling, as the name suggests, is simply sampling randomly from the population. It doesn't make any assumptions of sub-classifications about the given population and is only interested in sampling the population as a whole representative.
Stratified random sampling, as the name suggests, does sampling on strata( parts or subgroups divided in the population). The sampling then is done from each group proportionate to the group's size versus the population size.
One example is when you've got red, green and blue colored balls as the population and you want to sample the population based on colors, then the colored groups are strata and thus, the sampling will be done on subgroups and such type of sampling will be called as stratified random sampling.
Simple random sampling doesn't care about sub-properties in the population. It consider the population as whole and doesn't differentiate classes in the population, unlike stratified random sampling.
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https://brainly.com/question/4468092
