There are a variety of sampling methods that can be used including stratified random sampling, cluster sampling, and systematic sampling, which are types of probability sampling methods (Anderson, Sweeney, Williams, Camm, Cochran, Fry, & Ohlmann, 2020). There are also nonprobability sampling methods that can be used but should be used with caution - convenience sampling and judgment sampling (Anderson, et al., 2020). Stratified random sampling, cluster sampling, and systematic sampling can all use functions to prove whether the results of a study are "good", whereas convenience sampling is based off the convenience of selecting the sample and judgment sampling is based on the judgment of the person conducting the study (Anderson, et al., 2020). Convenience sampling and judgment sampling have too many unknowns based on how the samples are selected.

Stratified random sampling is used when a population is divided into strata and a sample is taken from each stratum and the variance in the stratum is small (Anderson, et al., 2020). For instance, separating a population into different groups based on education, income level, race or gender (Hayes, 2022). The U.S. Census is based, in part, on stratified sampling methods (United States Census Bureau, 2022). Stratified sampling methods are used to highlight differences between groups rather than the simple random sampling, which treats each group equally with the same likelihood of being sampled (Hayes, 2022).

Cluster sampling works the best when the elements in that cluster are different from each other (Anderson, et al., 2020). Cluster

sampling is used frequently in market research (Glen, n.d.). For instance, if you wanted to look at property taxes by county in a specific state, each county will have a variety of demographics for property prices that will differentiate one county from another. Please discuss with reference