Arizona State University (ASU) STP226 Elements of Statistics Exam 1 Practice

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How is cluster sampling structured?

By sampling individuals at random intervals

By dividing the population into segments and sampling the whole segments

Cluster sampling is structured by taking the entire population and dividing it into distinct segments or groups, known as clusters. Each cluster ideally serves as a mini-representation of the larger population. In this method, instead of sampling individuals randomly from the entire population, researchers select whole clusters and include all members of the chosen clusters in the sample.

This approach is particularly useful when dealing with large populations spread across a wide geographical area, as it can be more efficient and cost-effective. By sampling entire groups, researchers can gather data more quickly and with fewer resources compared to methods that require sampling individuals from the entire population.

Other sampling methods, such as random sampling or stratified sampling, have different structures that do not involve selecting and encompassing entire clusters, thus distinguishing cluster sampling's unique methodology.

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By selecting random individuals without grouping

By choosing members based on specific characteristics

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