What is stratified sampling and how is it used?
An example of an AI chatbot conversation about stratified sampling in statistics.
What is stratified sampling and how is it used?
Stratified sampling is a method of sampling that involves dividing a population into subgroups, or strata, that share similar characteristics. This technique ensures that each subgroup is adequately represented in the sample.
Can you give me an example of how it works?
Sure! For instance, if you want to survey students in a school, you might divide them into strata based on their grade levels. Then, you would randomly select a proportional number of students from each grade to ensure all grades are represented.
What are the advantages of using stratified sampling?
The main advantages include increased precision in estimates, better representation of the population, and the ability to analyze specific subgroups more effectively.
Are there any disadvantages to this method?
Yes, some disadvantages include the complexity of the sampling process and the need for detailed population information to create strata.
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