Which of the following is an example of non-random sampling?

Non-random sampling refers to methods where the selection of samples is not based on chance, and instead involves a systematic approach that may introduce bias or ensure representation of certain groups. Systematic sampling involves selecting every nth item from a list or a sequence, which means the choice of participants is based on a predetermined interval rather than randomness. This method can potentially lead to bias if the pattern used for selection aligns with periodic variations in the population.

In contrast, the other methods listed—stratified random sampling, cluster sampling, and simple random sampling—all employ mechanisms that enhance randomness and help ensure that every member of the population has an equal chance of being selected. Stratified random sampling divides the population into subgroups and randomly samples from each, while cluster sampling randomly selects entire groups, and simple random sampling involves selecting individuals completely at random. These techniques aim to achieve a representative sample without bias. Thus, systematic sampling distinctly qualifies as non-random because the selection process relies on a fixed interval, deviating from purely random selection.