Systematic sampling and cluster sampling are two different types of statistical measures used by researchers, analysts, and marketers to study samples of a population.
The way in which both systematic and cluster sampling pull sample points from the population is different. While systematic sampling uses fixed intervals from the larger population to create the sample, cluster sampling breaks the population down into different clusters.
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.
Systematic sampling and cluster sampling are both statistical measures used by researchers, analysts, and marketers to study samples of a population.
Systematic sampling involves selecting fixed intervals from the larger population to create the sample.
Cluster sampling divides the population into groups, then takes a random sample from each cluster.
Both systematic sampling and cluster sampling are forms of random sampling, known as probability sampling, which stands in contrast to non-probability sampling.
Systematic sampling and cluster sampling both have their advantages and disadvantages, but both can be time- and cost-efficient.