What is stratified sampling and can you give me an example?

What is stratified sampling and can you give me an example?

Stratified sampling is a method of sampling that involves dividing a population into subgroups, or strata, that share similar characteristics. For example, if you want to study the opinions of students in a university, you might create strata based on their year of study: freshmen, sophomores, juniors, and seniors.

How do you choose the strata?

Choosing strata depends on the research question. You should select characteristics that are relevant to the study. For instance, in a study about academic performance, you might stratify by major or GPA.

Can you explain how to conduct stratified sampling?

Sure! First, identify the strata. Then, determine the sample size for each stratum, often proportional to the stratum's size in the population. Finally, randomly select samples from each stratum.

What are the advantages of using stratified sampling?

Stratified sampling increases the precision of your estimates by ensuring that all subgroups are represented. It can also reduce variability and improve the overall quality of the data.