Reviewed by Oct 05, 2020| Updated on
Systematic sampling is a probability sampling method in which sample members are chosen from a larger population as per a random starting point but with a periodic, fixed interval. This interval, termed as the sampling interval, is determined by dividing population size by sample size as desired.
With the sample population being chosen in advance if the periodic interval is calculated in advance, and the starting point is random, systematic sampling is still assumed to be random. There are many methods of sampling a population for statistical inference; one type of random sampling is a systematic sampling.
As simple random population sampling can be inefficient and time-consuming, statisticians turn to other approaches, such as systematic sampling, for example. Choosing a sample size can be done quickly through a systematic approach. Once a fixed starting point is established, a constant interval is chosen to encourage the selection of the participants.
Systematic sampling is superior to simple random sampling when the chance of manipulation of the data is small. If such a risk is high because a researcher can control the interval length to get desired results, it would be more fitting to use a simple random sampling technique.
Due to its simplicity, systematic sampling is popular with researchers and analysers. Researchers usually presume that the findings are indicative of most normal populations unless there is overwhelmingly one random feature for each "nth" data sample (which is unlikely).
In other words, a population will exhibit a natural degree of randomness along the metric chosen. If the population has a form of a uniform template, the possibility of very common cases being mistakenly selected becomes more evident.
One threat that statisticians should consider when conducting systematic sampling is organising the list that was used with the sampling interval. If the population on the list is organised in a cyclical pattern that matches the sampling interval, then the sample selected may be biased.