Simple random sampling is a probability sampling technique wherein each individual or element in a population has an independent and equal chance of getting selected for the sample. The process involves selecting individuals at random from the entire population. Simple random sampling is a straightforward and unbiased way to create a representative sample from a larger population.

Advantages of simple random sampling include:

Unbiased representation: Simple random sampling provides an unbiased population representation, making it suitable for generalizing findings.

- Statistical inference: The equal probability of selection facilitates statistical analysis and inference, allowing researchers to make valid generalizations about the population.
- Simplicity: It is a straightforward method that is easy to understand and implement.

Some disadvantages of simple random sampling include:

- Practical challenges: Creating a complete sampling frame in large populations may be impractical or expensive.
- Logistical considerations: Random selection methods may require careful planning and execution, especially in situations with limited resources or access to the entire population.
- Potential for sampling bias: If the sampling frame is not comprehensive or there are issues with the randomization process, there is a risk of sampling bias.

Simple random sampling is useful for seeking unbiased representation and statistical inference in research. Researchers often use this method when the population is relatively small and it is feasible to create an exhaustive sampling frame.

**List of recommended resources** #

### For a broad overview #

2.4 – Simple Random Sampling and Other Sampling Methods

This guide by PennState Eberly College of Science gives an overview of the 5 main types of probability sampling, namely simple random sampling, stratified sampling, systematic sampling, cluster sampling and multistage sampling, along with examples.

Simple Random Sampling | Definition, Steps & Examples

This blog by Lauren Thomas on Scribbr gives an overview of simple random sampling, one of the most straightforward types of probability sampling. The blog discusses when to use simple random sampling and how to carry out this probability sampling method.

### For in depth understanding #

Advanced Sampling Theory with Applications

Chapter 2 and 3 of this book by Sarjinder Singh focus on, and provide an in-depth understanding of, simple random sampling and the use of auxiliary information in simple random sampling.

Choosing the type of probability sampling

This chapter from the book Sampling Essentials by Sage Publications gives an in-depth understanding of the different types of probability sampling as well as the differences between them. It also details the strengths and weaknesses of each probability sampling method.

### Case study #

In this article by P. W. West, simple random sampling methods are considered when there is no list available for sampling units. The article, in particular, takes the case of an inventory of large forests or other populations where a list of individual plants is not available and only a map of the area is available.

This paper by Astin Lukum Arifin Ansar and Yudith J. Dengo aims to figure out the influence of school culture on the performance of high school English teachers in the Gorontalo Province in Indonesia. The sample of the research was taken from 123 high school English teachers in Gorontalo Province using the simple random sampling technique based on Slovin’s formula.

**References** #

Simple Random Sampling: 6 Basic Steps With Examples

Simple random sampling: Definition, examples, and how to do it