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. Among the various sampling methods used by researchers, the random sampling method is a commonly used sample collection method. Here, each member of the population has an equal chance of being selected.

For example, let us see how a random sampling method can determine the mean number of college professors' books in their offices in a particular college. Suppose 25 professors are randomly selected and asked about the number of books in their office. The data obtained shall give a statistical result of the number of books owned by each professor in the entire college. Another example is determining the average cost of a two-day stay in a hospital in Massachusetts by conducting surveys of 100 hospitals across the state, selected using simple random sampling.

The probability sampling method is similar to random sampling but has significant differences. The probability of selection of each member of the population is known but not necessarily the same. Suppose a college has 10,000 part-time students (the population). To determine the average amount of money a part-time student spends on books in the fall term, the probability sampling method can be used. A sample of 100 different part-time students from the ten disciplines is randomly selected such that at least one student from each discipline is sampled. Several students from each discipline being selected has a fixed but not necessarily an equal chance of being selected. This is an example of the probability sampling method.

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