What is the difference between stratified sampling and cluster sampling?
An example of an AI chatbot conversation about sampling methods in statistics.
What is the difference between stratified sampling and cluster sampling?
Stratified sampling involves dividing a population into subgroups and sampling from each, while cluster sampling involves dividing the population into clusters and sampling entire clusters.
Can you give me an example of each method?
Sure! In stratified sampling, if you have a population of students, you might divide them by grade and sample from each grade. In cluster sampling, you might randomly select a few classrooms and include all students in those classrooms.
Which method is better for a small population?
For a small population, stratified sampling is often better as it ensures representation from all subgroups, while cluster sampling might not capture diversity.
Are there any disadvantages to either method?
Yes, stratified sampling can be more complex and time-consuming, while cluster sampling can lead to higher sampling error if clusters are not representative.
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