What Is a Random Sample in Psychology?

Random sampling is a technique in which each person is equally likely to be selected. Simply put, a random sample is a subset of individuals randomly selected by researchers to represent an entire group. The goal is to get a sample of people representative of the larger population.

It involves determining the target population, determining the number of participants you need, and then picking participants in a way that gives everyone an equal chance of being selected at random.

Example of Random Sampling in Psychology

For example, if researchers were interested in learning about alcohol use among college students in the United States, the larger population (in other words, the "group of interest"), would be made up of every kid in every college and university in the country.

It would be virtually impossible to interview each and every one of these people to find out if they drink, what types of alcohol they drink, how often, under what circumstances, how much (a beer or two per week versus enough to get intoxicated every weekend), and so forth.

Types of Random Sampling in Psychology

Random sampling can occur in a few different ways:

• Simple random sampling involves having a list of each member of the target population. Each person is assigned a number, and then the necessary number of participants is randomly chosen using a lottery method.
• Stratified random sampling involves dividing members of the target population into homogenous groups based on specific characteristics of interest (such as age, gender, diagnosis, etc), and then drawing a random sample from each sub-group.
• Cluster random sampling involves dividing the larger population into smaller clusters that are representative of the larger group. Researchers then draw a random number of clusters to be included in the sample.
• Systematic random sampling involves using a systematic sampling method, like selecting every 15th member of the group to be included in the sample. 

How Researchers Create Random Samples

Random sampling can be costly and time-consuming. However, this approach to gathering data for research does provide the best chance of putting together an unbiased sample that is truly representative of an entire group as a whole.

Returning to the imaginary study of alcohol use among college students, here's how random sampling might work:

Determine the Total Population

According to the National Center for Education Statistics ( NCES), approximately 19.7 million students were enrolled in U.S. colleges and universities in 2020, the most recent statistics available. These 20 million individuals represent the total population to be studied.

Determine the Characteristics of the Population

For the purpose of drawing a random sample of this group, all students must have an equal chance of being selected. For example, scientists conducting the study would need to make sure that the sample included the same percentage of men and women as the larger population.

According to the NCES statistics, 11.3 million of the total population of college students are female, and 8.5 million are male. The sample group would need to reflect this same ratio of women to men.

Represent All Characteristics In the Random Sample

Besides gender, researchers would also want to go through the same process for other characteristics—for example, race, cultural background, year in school, socioeconomic status, and so forth, depending on the specific purpose of the study.

For instance, if they wanted to home in on alcohol use among Asian students, they would create a random sample consisting only of Asian students. By the same token, if the study were focused on how much students drink during the week, they would create a questionnaire or other method for finding only kids who drink on weekdays for their research.

Pros and Cons of Random Sampling in Psychology

The most significant benefit of random sampling is that it helps reduce the risk of bias. Researchers also suggest that it can improve statistical analysis and allow them to quantify non-responses.

This type of sampling tends to be more costly in terms of money, time, and resources. Because of this, researchers sometimes have to utilize other sampling methods.