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Systematic sampling is a statistical method involving the selection of every k^th element from a sampling frame, where k, the sampling interval, is calculated as:

k = population size (N) / sample size (n)

Using this procedure each element in the population has a known and equal probability of selection. This makes systematic sampling functionally similar to simple random sampling. It is however, much more efficient (if variance within systematic sample is more than variance of population) and much less expensive to carry out.

The researcher must ensure that the chosen sampling interval does not hide a pattern. Any pattern would threaten randomness. A random starting point must also be selected.

Systematic sampling is to be applied only if the given population is logically homogeneous, because systematic sample units are uniformly distributed over the population.

Example: Suppose a supermarket wants to study buying habits of their customers, then using systematic sampling they can choose every 10^th or 15^th customer entering the supermarket and conduct the study on this sample.

This is random sampling with a system! From the sampling frame, a starting point is chosen at random, and thereafter at regular intervals. For example, suppose you want to sample 8 houses from a street of 120 houses. 120/8=15, so every 15th house is chosen after a random starting point between 1 and 15. If the random starting point is 11, then the houses selected are 11, 26, 41, 56, 71, 86, 101, and 116. If there were 125 houses, 125/8=15.625, so should you take every 15th house or every 16th house? If you take every 16th house, 8*16=128 so there is a risk that the last house chosen does not exist. To overcome this the random starting point should be between 1 and 10. On the other hand if you take every 15th house, 8*15=120 so the last five houses will never be selected. The random starting point should now be between 1 and 20 to ensure that every house has some chance of being selected.