Which sampling method uses a fixed interval from a randomly chosen start point?

The sampling method that utilizes a fixed interval from a randomly chosen starting point is known as systematic sampling. In this approach, once an initial random point has been established, subsequent samples are selected by consistently applying the fixed interval. For example, if the starting point is selected randomly within a dataset, selecting every nth item thereafter (such as every 10th item) would complete the systematic sampling process. This method is efficient and allows for a straightforward way to select samples without the need for complex randomization once the starting point is determined.

Other sampling methods like cluster sampling involve dividing a population into clusters and then randomly selecting entire clusters, while stratified sampling focuses on dividing the population into strata and ensuring that samples represent each stratum. Multistage sampling incorporates elements of both cluster and stratified sampling, which can complicate the sample selection process further. Systematic sampling stands out for its simplicity and efficiency through the use of fixed intervals.