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In this question, your friend is conducting a survey by randomly selecting 50 students from each of the first-year, second-year, third-year, and fourth-year groups. This method is an example of a **stratified sample**.

A **stratified sample** is used when the population is divided into distinct subgroups (called strata), and then a random sample is taken from each stratum. In this case, the strata are the different year levels of students (first-year, second-year, etc.).

Would you like a more detailed explanation or have any other questions? 

Here are five related questions to explore:
1. What is the difference between a stratified sample and a simple random sample?
2. How would you determine the appropriate sample size for each stratum in a stratified sample?
3. What are the advantages of using stratified sampling over cluster sampling?
4. In what situations would stratified sampling be preferable to other sampling methods?
5. How can you ensure that your random selection from each stratum is truly random?

**Tip:** Stratified sampling is especially useful when you want to ensure representation from all subgroups in a population.

A friend of yours in your intro stats class obtains permission to randomly sample the University student body to conduct a satisfaction survey on some recent changes to the enrollment process. She randomly samples 50 first-year students, 50 second-year students, 50 third-year students, and 50 fourth-year students. This is an example of a ______ sample.

Learn about stratified sampling through this university-level statistics problem, where students are divided into year levels and random samples are taken from each group. This problem explains key concepts in sampling methods, including stratified sampling.

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