What type of random sampling involves selecting a starting point and then every nth individual?

Systematic random sampling is a method where you select a starting point at random and then choose every nth individual from a larger population. This technique is useful for its simplicity and efficiency, particularly in scenarios where a complete list of the population is available. The process involves determining the interval (n) based on the size of the population and the sample you want to achieve.

For example, if you have a list of 100 names and want to sample 10, you might choose every 10th name after a random starting point, ensuring that the sample can be easily replicated. This method provides a way of introducing randomness into the selection process while still maintaining a structured approach.

In contrast, other sampling methods, such as cluster random sampling, involve dividing the population into separate groups (clusters) and then randomly selecting whole clusters to include in the sample. Stratified random sampling is focused on ensuring that specific subgroups within a population are adequately represented, often by dividing the population into strata and sampling from each stratum. Simple random sampling gives every individual in the population an equal chance of being selected but does not follow a systematic approach.

Understanding these different sampling methods is crucial in research and analytics, as the sampling method can affect the validity and reliability of the