Systematic sampling and cluster sampling differ in how they pull sample points from the population included in the sample. Cluster sampling breaks the population down into clusters, while systematic sampling uses fixed intervals from the larger population to create the sample. Systematic sampling selects a random starting point from the population, and then a sample is taken from regular fixed intervals of the population depending on its size. Cluster sampling divides the population into clusters, and then takes a simple random sample from each cluster.
Cluster sampling is considered less precise than other methods of sampling. However, it may save costs on obtaining a sample. Cluster sampling is a two-step sampling procedure. It may be used when completing a list of the entire population is difficult. For example, it could be difficult to construct the entire population of the customers of a grocery store to interview. However, a person could create a random subset of stores, which is the first step in the process. The second step is to interview a random sample of the customers of those stores. This is a simple manual process that can save time and money.
In systematic sampling, a random starting point in the population is selected. Then, the nth subject from the population is selected to be included in the sample. This creates a sample that is representative of the entire sample, as long as certain important characteristics of the population are not repeated at the same interval as that used in the sample selection process. Systematic sampling is simple and allows for a degree of process to be used in selecting the sample. This process also guarantees the entire population is evenly sampled. Systematic sampling is useful for certain purposes in finance.