When is it better to use systematic over simple random sampling?

Under simple random sampling, a sample of items is chosen randomly from a population, and each item has an equal probability of being chosen. Simple random sampling uses a table of random numbers or an electronic random number generator to select items for its sample. Systematic sampling involves selecting items from an ordered population using a skip or sampling interval. The use of systematic sampling is more appropriate compared to simple random sampling when a project's budget is tight and requires simplicity in execution and understanding the results of a study. Systematic sampling is better than random sampling when data does not exhibit patterns and there is a low risk of data manipulation by a researcher.

Simple random sampling requires that each element of the population be separately identified and selected, while systematic sampling relies on a sampling interval rule to select all individuals. If the population size is small or the size of the individual samples and their number are relatively small, random sampling provides the best results. However, as the required sample size increases and a researcher needs to create multiple samples from the population, this can be very time-consuming and expensive, making systematic sampling a preferred method under such circumstances.

Systematic sampling is better than simple random sampling when there is no pattern in the data. However, if the population is not random, a researcher runs the risk of selecting elements for the sample that exhibit the same characteristics. For instance, if every eighth widget in a factory was damaged due to a certain malfunctioning machine, a researcher is more likely to select these broken widgets with systematic sampling than with simple random sampling, resulting in a biased sample.

Systematic sampling is preferable to simple random sampling when there is a low risk of data manipulation. If such a risk is high when a researcher can manipulate the interval length to obtain desired results, a simple random sampling technique would be more appropriate.