The example of stratified sampling is a health educator wanted to study the sleeping habits of the undergraduate students in her study.
For her study, the researcher chose a simple random sample of size 150 from each of the classes (150 freshmen, 150 sophomores, 150 juniors, and 150 seniors) for a total of 600 sampled students. a poll asked a random sample of 1,112 adults whether they believe that the use of marijuana for medical reasons should be legalized.
<u>Sampling:</u>
In statistics, quality assurance, and research methodologies, sampling is the selection of a subset of individuals (statistical sample) from a statistical population in order to estimate characteristics of the population as a whole. A statistician tries to collect a representative sample of the population in question. Sampling is less costly than measuring the entire population, provides faster data collection, and can provide insight when the entire population cannot be measured.
<u>Stratified Sampling:</u>
When the population embraces a number of distinct categories, the frame can be organized by these categories into separate "strata." Each stratum is then sampled as an independent sub-population, out of which individual elements can be randomly selected. The ratio of the size of this random selection (or sample) to the size of the population is called a sampling fraction. There are several potential benefits to stratified sampling.
There are, however, some potential drawbacks to using stratified sampling.
First, identifying strata and implementing such an approach can increase the cost and complexity of sample selection, as well as leading to increased complexity of population estimates.
Second, when examining multiple criteria, stratifying variables may be related to some, but not to others, further complicating the design, and potentially reducing the utility of the strata.
Finally, in some cases (such as designs with a large number of strata, or those with a specified minimum sample size per group), stratified sampling can potentially require a larger sample than would other methods (although in most cases, the required sample size would be no larger than would be required for simple random sampling).
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