Types of Probability Sampling

Cluster sampling is a sampling technique in which clusters of participants that represent the population are identified and included in the sample

Three examples of probability sampling include: cluster sampling, which involves randomly choosing clusters, or natural divisions; multistage sampling, which involves randomly choosing a sample from each cluster; and multiphase sampling, which involves gathering data from a large sample and then gathering additional data from a smaller sub-sample.

A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. An example of a simple random sample would be the names of 25 employees being chosen out of a hat from a company of 250 employees.

Stratified sampling is a probability sampling technique wherein the researcher divides the entire population into different subgroups or strata, then randomly selects the final subjects proportionally from the different strata.

Systematic sampling is a probability sampling method in which a random sample from a larger population is selected.