Let's identify the sampling methods used in each of the scenarios:

2. Convenience Sampling

   • The person is collecting data from her neighborhood, which is based on convenience since she is sampling those who are nearby rather than randomly selecting from the entire town.

3. Stratified Sampling

   • The human resources employee is selecting a random sample from each department, which constitutes dividing the population into strata (departments) and then taking a random sample from each stratum.

4. Cluster Sampling

   • The flight attendant selects 15 entire flights and uses the data from all passengers on those flights. This is an example of cluster sampling, where entire groups (flights) are selected randomly, and data from all members of those groups are used.

5. Simple Random Sampling

   • The company randomly selects 75 products by assigning numbers and using a random selection process. Each product has an equal chance of being chosen, which aligns with simple random sampling.

6. Systematic Sampling

   • The provost is selecting every 6th faculty member from an alphabetic list. This is systematic sampling, where a fixed interval is used to choose participants from an ordered list.

Summary:

• 2) Convenience Sampling
• 3) Stratified Sampling
• 4) Cluster Sampling
• 5) Simple Random Sampling
• 6) Systematic Sampling

Would you like further details or clarifications?

Here are 5 related questions to deepen your understanding:

1. What are the advantages and disadvantages of using convenience sampling?
2. How does stratified sampling ensure representation from different groups?
3. What are the differences between cluster and stratified sampling?
4. Why might systematic sampling introduce bias in some cases?
5. In which real-world situations would you use simple random sampling?

Tip: When using systematic sampling, ensure that the list has no hidden patterns that could bias the results (e.g., sorting by a factor related to the study).