Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.

Systematic sampling is one of the simplest methods of sample collection. In systematic sampling, sample selection is based on the position and not on sample characteristics such as color, gender, or physical appearance. The population under study is arranged according to an ordering scheme, and the samples are selected at regular intervals through that ordered list.

Systematic sampling includes the following steps:

1. The population size divided by the sample size gives the sampling interval. If the value of the sampling interval is in decimals, it is rounded to the nearest integer.
2. A random starting point is chosen to represent the sample's first member. The sampling interval is used in order to choose the other samples.

Let us consider an example of a phone survey. The phone book contains 20,000 residence listings. Four hundred names are needed to form a sample. The population is numbered between 1–20,000, and then a simple random method is used to pick the first name of the sample. After that, every fiftieth name is chosen until a total of 400 names are covered. Systematic sampling is frequently used for sample collection because it is a convenient method to execute.

This text is adapted from Openstax, Introductory Statistics, Section 1.2 Data, Sampling, and Variation in Data and Sampling