Machine Learning Resources

What is Cluster Sampling?

At a high level, sampling can be divided into probability sampling and non-probability sampling. Probability sampling means that each unit in the population has some probability of being selected. The most common types of probability sampling include:

Cluster Sampling: In a cluster sample, subunits of the population that are hopefully independent of each other and representative of the population as a whole are identified. The choosing of the subunits is done randomly, which can be thought of as a simple random sample at the outer level. Once the sub-units are chosen, all of the members of the chosen units are then sampled. For example, if all students at a university, regardless of major or class level, lived in different dorms on campus, a cluster sample could be conducted by choosing a subset of dorms and then sampling all students who live in those buildings. 

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