.

Image Source: Statistical Aid: A School of Statistics

Simple random sampling is considered the easiest and most popular method of probability sampling. To perform simple random sampling, all a researcher must do is ensure that all members of the population are included in a master list, and that subjects are then selected randomly from this master list.

While simple random sampling creates samples that are highly representative of the population, it can be time consuming and tedious when creating large samples.

**The following 8-step procedure may be followed in drawing a simple random sample of n units from a population of N units.**

- Assign serial numbers to the units in the population from 1 through N.
- Decide on the random number table to be used.
- Choose an N-digit random number from any point in the random number table.
- If this random number is less than or equal to N, this is your first selected unit.
- Move on to the next random number not exceeding N, vertically, horizontally or in any other direction systematically and choose your second unit.
- If at any stage of your selection, the random number chosen exceeds N, discard it and choose the next random number.
- If, further, any random number is repeated, it must also be discarded and be replaced by a fresh random number appearing next.
- The process stops once you arrive at your desired sample size.

**There are two approaches that aim to minimize any biases in the process of simple random sampling:**

**Application of simple random sampling**

- A list of all members of population is prepared. Each element is marked with a specific number (suppose from 1 to
*N*). -
*n*items are chosen among a population size of*N.*This can be done either with the use of random number tables or random number generator software. - The Aromatic Company is planning to conduct a study to estimate the proportion of toilet soap users who prefer a certain color or flavor of their product. A simple random sample of customers may be used for this purpose. It is assumed in this case that a list (sampling frame) of the consumers is available to the research team.
- A forester in Chittagong Hill Tracts may wish to estimate the volume of timber or proportion of diseased trees in a forest by se geographic points in the area covered by the forest and then attaching a plot of fixed size and shape to that point. All the trees within the sample plots may be studied. But again the basic design is a simple random sample.

**Advantages of Simple Random Sampling**

- It is a fair method of sampling and if applied appropriately it helps to reduce any bias involved as compared to any other sampling method involved.
- Since it involves a large sample frame it is usually easy to pick smaller sample size from the existing larger population.
- The person who is conducting the research doesn’t need to have a prior knowledge of the data being collected. One can simply ask a question to gather the researcher need not be a subject expert.
- This sampling method is a very basic method of collecting the data. There is no technical knowledge required and need basic listening and recording skills....(more)

- How social media can help you find jobs that aren't advertised
- Insightsoftware acquisition of Izenda targets embedded BI
- Top 20 cloud computing skills to boost your career in 2021
- Will codeless test automation work for you?
- Reap the rewards of IT/OT convergence in manufacturing
- New IoT Cybersecurity Improvement Law is a start, not a final solution
- Who belongs on a high-performance data governance team?
- Interpreted vs. compiled languages: What's the difference?
- IBM acquires MyInvenio to build its automation portfolio
- Structured vs. unstructured data: The key differences

Posted 12 April 2021

© 2021 TechTarget, Inc. Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central