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Simple random sampling is atechniquein which each member of a population has an equal chance of being chosen through an unbiased selection method. Each subject in the sample is given a number, and then the sample is chosen randomly.
This method is considered “simple” because it’s straightforward and implements a random process (like flipping a coin or drawing numbers from a hat) to select the sample.
The random sampling method is one of the simplest and most common forms of collecting data, as it provides an unbiased representation of a group. The random subset of selected individuals represents 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.
Example
For example, if you wanted to conduct a survey about food preferences in a school of 1000 students, and you wanted to sample 100 students.
The students assigned those numbers would be the ones you survey.
Method
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 give each individual an equal chance of being chosen.
This is meant to represent 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.
Representativeness
Random sampling ensures that every member of the target population has an equal chance of being selected. This helps to ensure that the sample is representative of the population, making it more likely that the findings can be generalized to the entire population.
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 accurately represent the population as a whole. If this occurs, the researcher would need to restart the sampling process.
Other techniques
There are four types of random sampling techniques (simple, stratified, cluster, and systematic random sampling.
Stratified Random Sampling
Cluster Random Sampling
Systematic Random Sampling
Key Terms
References
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/
Saul McLeod, PhD
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
Olivia Guy-Evans, MSc
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
Julia Simkus
BA (Hons) Psychology, Princeton University
Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master’s Degree in Counseling for Mental Health and Wellness in September 2023. Julia’s research has been published in peer reviewed journals.
