Compare the different types of random sampling methods. Describe examples in which stratified
sampling and cluster sampling should be used. Do not cite examples already provided by peers.
In replies to peers, provide analysis and recommendations.
There are four methods of random sampling: simple random, systematic, stratified, and cluster
In a simple random sample, every member of the population has an equal chance of being
selected and can be conducted with tools such as a random number generator. Systematic
sampling is similar to simple random sampling with every member of the population being listed
with a number, but instead of being randomly generated, they are chosen at regular intervals.
Stratified sampling involves dividing the population into subpopulations that differ in important
ways. More precise conclusions are drawn if every subgroup is properly represented in the
sample. Cluster sampling also involves dividing the population into subgroups, but each
subgroup should have similar characteristics to the whole sample. Instead of sampling
individuals from each subgroup, you randomly select entire subgroups.
Stratified sampling example:
The company has 800 female employees and 200 male
employees. You want to ensure that the sample reflects the gender balance of the company, so
you sort the population into two strata based on gender. Then you use random sampling on each
group, selecting 80 women and 20 men, which gives you a representative sample of 100 people.
Cluster sampling example:
The company has offices in 10 cities across the country (all with
roughly the same number of employees in similar roles). You don't have the capacity to travel to