A** random sample** is a subset of individuals that are randomly selected from a population of interest. Because researchers usually cannot obtain data from every single person in a group due to time, costs and other constraints, a smaller portion is randomly chosen to represent the entire group as a whole. The goal is to get a sample that is representative of the larger population.

For example, imagine that a market research company is interested in learning more about what type of dish soap people use.

Instead of polling every single individual, they will select a random sample of people and question them about the kind of soap they use. By using a randomized sample, they get a clearer idea of what likely exists in the population as a whole.

### How Do Random Samples Work?

As previously mentioned, a random sample is drawn from a pool of individuals that make up a larger population. What exactly do we mean by population? Essentially, the population is the total group of interest. For example, if you were interested in studying drug use among U.S. adults, every adult over the age of 18 currently living in the United States would represent the total population.

A sample is a smaller subset drawn from that larger population. Because the sample is random, every single member of the group stands an equal chance of being selected to be in the sample.

### An Example of a Random Sample

Let's imagine that you want to conduct a study looking at alcohol use among U.S.

college students. According to the National Center for Education Statistics, approximately 20.2 million students were enrolled in U.S. colleges and universities in 2015. These 20 million plus individuals represent the total population that you want to study.

In drawing a random sample, all students must have an equal chance of being selected for the study.

When dealing with an enormous population, random sampling can be very costly and time-consuming. However, this method does provide the best chance of obtaining an unbiased sample that is truly representative of the entire group as a whole.

The purpose of using random sampling is to obtain a representative sample of the entire group. For a random sample to be representative, it must reflect the qualities present in the larger population.

In our imaginary study of alcohol use among U.S. college students, researchers would need to make an effort to ensure that their sample contains the same percentage of men and women as the larger population. They would also want to ensure that the random sample also included the same percentages of different ethnic and cultural backgrounds.

For example, around 11.5 of the total population of college students are female, and 8.7 million of them are male. The sample drawn from the group should reflect the same numbers seen in the larger population.

### Why Use a Random Sample

The principal advantage of using random sampling is that it can lead to a representative and less prone to sampling biases.

The major disadvantage is that it requires a great deal of time, money and effort, particularly when dealing with large populations.

**More Psychology Definitions:** The Psychology Dictionary

_{References}

_{National Center for Education Statistics. (2015). Fast facts: Back to school statistics. Retrieved from http://nces.ed.gov/fastfacts/display.asp?id=372.}