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Simple Random Sampling: Definition, Steps and Examples

By Julia Simkus, published Jan 26, 2022


Simple random sampling is a sampling technique in which each member of a population has an equal chance of being chosen, through the use of an unbiased selection method. Each subject in the sample is given a number and then the sample is chosen by a random method.

simple random sampling

Key Terms
  • A sample is the participants you select from a target population (the group you are interested in) to make generalizations about. As an entire population tends to be too large to work with, a smaller group of participants must act as a representative sample.
  • Representative means the extent to which a sample mirrors a researcher's target population and reflects its characteristics (e.g. gender, ethnicity, socioeconomic level). In an attempt to select a representative sample and avoid sampling bias (the over-representation of one category of participant in the sample), psychologists utilize a variety of sampling methods.
  • Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

The random sampling method is one of the simplest and most common forms of collecting data as it is meant to provide an unbiased representation of a group. The random subset of selected individuals is used to represent an entire data set.

The goal of simple random sampling is to create a manageable, balanced subset of individuals that is representative of a larger group that would otherwise be too challenging to sample.

How to random sample?

  1. First, choose the target population that you wish to study and determine your desired sample size. The smaller the sample size the less likely it can be generalised to the wider research population and is unlikely to be fully representative.
  2. The list of the people from which the sample is drawn is called the sampling frame. Examples of samplong frames include the electoral register, schools, drug addicts etc.).
  3. Then, assign a sequential number to each subject in the sampling frame.
  4. Next, individuals are selected using an unbiased selection method. Some examples of simple random sampling techniques include lotteries, random computer number generators, or random draws.

Advantages

Minimizes Bias

It is the least biased sampling method as every member of the target population has an equal chance of being chosen. The purpose of simple random sampling is to provide each individual with an equal chance of being chosen.

This is meant to provide a representation of a group that is free from researcher bias. Like any sampling technique, there is room for error, but this method is intended to be an unbiased approach.

Limitations

Expensive and time-consuming

It is a very expensive and time consuming method, it is difficult to get the name of every member of the target population especially if it is a very large population so it is rarely used.

Access to respondents

This is actually quite hard to achieve - especially if the parent population is large. Since the participants do not volunteer to participate, it can be challenging for researchers to gain access to respondents when drawing from a large population.

Sampling error

Sampling errors can occur when the sample does not end up accurately representing the population as a whole. If this occurs, the researcher would need to restart the sampling process.

Other types of random sampling techniques:

There are 4 types of random sampling techniques (simple, stratified, cluster, and systematic random sampling.

  1. Stratified Random Sampling
    • In stratified random sampling, researchers will first divide a population into subgroups, or strata, based on shared characteristics and then randomly select among these groups.
    • This method is typically used when a population has distinct differences, such as demographics, level of education, or age, and can easily be broken into subgroups.
  2. Cluster Random Sampling
    • Similar to stratified random sampling, cluster random sampling begins by dividing a population into smaller groups.
    • However, in cluster sampling, researchers use naturally formed groups to divide a large population up into clusters, and then select randomly among the clusters to form the sample.
    • Examples of these pre-existing groups could include school districts, city blocks, or households.
  3. Systematic Random Sampling
    • Systematic random sampling involves taking random samples at regular periodic intervals.
    • For example, if you were conducting a survey in a cafeteria, you could give a survey to every sixth customer that comes into the cafeteria.

About the Author

Julia Simkus is an undergraduate student at Princeton University, majoring in Psychology. She plans to pursue a PhD in Clinical Psychology upon graduation from Princeton in 2023. Julia has co-authored two journal articles, one titled “Substance Use Disorders and Behavioral Addictions During the COVID-19 Pandemic and COVID-19-Related Restrictions," which was published in Frontiers in Psychiatry in April 2021 and the other titled “Food Addiction: Latest Insights on the Clinical Implications," to be published in Handbook of Substance Misuse and Addictions: From Biology to Public Health in early 2022.

How to reference this article:

Simkus, J. (2022, Jan 26). Simple Random Sampling: Definition, Steps and Examples. Simply Psychology. www.simplypsychology.org/simple-random-sampling.html

Sources

Hayes, A. (2021). Simple Random Sample. Investopedia. Retrieved from https://www.investopedia.com/terms/s/simple-random-sample.asp

Simple random sample: Definition and examples. Statistics How To. (n.d.). Retrieved from https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/simple-random-sample/

Simple random sampling: Definition, examples, and how to do it. Qualtrics. (2022). Retrieved from https://www.qualtrics.com/experience-management/research/simple-random-sampling/ nce-management/research/simple-random-sampling/

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