Simple Random Sampling : Definition, Method & Examples

The Ultimate Guide to Random Sampling

Get a step-by-step guide for choosing the correct representative sample for survey research.

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Table of Contents

What is Simple Random Sampling?

Simple random sampling is a sampling method used in market research studies that falls under the category of probability sampling. This means that when employed, simple random sampling gives everyone in the target population an equal and known probability of being selected as a respondent in the sample group.

Simple random sampling assigns numbers to everyone within the population, so that a sample group may be selected using processes that pick random numbers from the list.

This method of sampling is most apt for when the main objective of a study is for its findings to be generalizable for a whole population. In other words, this method of sampling ensures that the data extracted from the chosen sample group is reflective of what it would be for the target population as a whole.

Simple random sampling is usually used for large populations, hence, it is important to ensure a sample size that is large enough to fittingly represent this population otherwise it will lead to increase in margin of error.

Simple random sampling example

Simple random sampling ensures that all the participants have a fair chance of being chosen for the research or study. 

Example

A retail chain uses simple random sampling to assess the sales of all its branches. The retail chain company can randomly select its branches for 6 months to conduct detailed market research. 

Population: In this situation, the population is all the branches of the retail company. Let’s say the retail company has 150 branches in the entire country. 

Sample: With simple random sampling, the company selects 85 branches for the market research. 

Data collection: To collect data, the company sends a survey to the managers of each retail branch. 

Sampling Methods Guide

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How to create a sample using Simple Random Sampling?

In order to use simple random sampling in a market research tool, a researcher must know the size of the population in order to ascertain the number of total units in the population. After finding the total number of units/people within the population, the researcher must then assign serial numbers to each one of them.

For example, if the study is on the employees of an organization that has 300s employees, each one of them must be assigned a number as they are the population from which the sample must be drawn.

After the numbers have been assigned, there are a few common ways in which the required sample size can be drawn from the population:

  • Lottery method:

This method involves all the serial numbers being written down on chits of paper and being put into a container that is properly mixed manually. Then, chits are picked out of the container to select the sample group.

  • Software packages:

Different market research tools are used by researchers to pick a sample group. Excel and SPSS are the most common. Necessary commands must be input in regard to population size and required sample size.

  • Random number tables:

Random number tables, such as the one below ranging from 000-300, can also be used. After creating a random number table with the serial numbers of your target population, you may select specific columns/rows for your sample group according to your decided sample size.

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When can Simple Random Sampling be used?

Simple random sampling can generate better results for your survey research when it is accompanies with the following 

  • Survey Software :

Using a survey software has a tools to conduct online surveys, CATI surveys and survey analytics can help in conducting simple random sampling studies easily. You can create a panel randomly using a survey panel manager and conduct you research by distributing the survey from the market research tool. When you have the time, resources, an omnichannel market research tool, and financial means/funding to carry it out.

  • Target Population Information :

When there is access to a complete list of everyone in the target population, it is simple to pick out a random sample from the complete list. It can be done using the three ways that we discussed above. 

  • Accessibility :

When there is a way to contact/access everyone who is selected as a respondent in the sample group, it is easy to conduct simple random sampling studies. 

What are the advantages of using Simple Random Sampling?

  1. The findings are highly generalizable for the whole target population.
  2. It is easily understood and the results are highly projectable. In contrast to other sampling methods, this method doesn’t require additional steps such as breaking down the population into sub-populations. 
  3. Relative to other methods of sampling, simple random sampling has low bias. It fully eliminates human bias as samples are selected using random selection.
  4. The data collected through simple random sampling tends to be well informed and holistic.

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Disadvantages of Simple Random Sampling

  1. It may not be as efficient as other sampling methods, such as stratified sampling.
  2. The potential for sampling error may exist as the use of ‘random selection’ may lead to the sample group not being reflective of the population. This is especially the case when the sample set of the target population is already not inclusive enough. Therefore, there is no assurance of “representativeness”. 
  3. It is not a favourable method of sampling in cases where the target population is very large and/or is widely geographically dispersed.

This method of sampling is also quite expensive, therefore in cases where cost is a primary consideration due to limited resources or funding, this isn’t a feasible sampling method.

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How is Simple Random Sampling different from other Probability Sampling?

Simple Random Sampling Vs. Stratified Sampling:

In stratified sampling, a researcher breaks down the targeted population into pre-define strata. The strata are built based on shared characteristics. 

On the other hand, simple random sampling extracts samples from the total population irrespective of common characteristics. 

Researchers use stratified sampling when a particular sub-group needs to be represented in the research. 

This sampling method often leads to bias because researchers apply their reasonings to create strata. 

[Read more on Stratified Sampling]

Simple Random Sampling Vs. Cluster Sampling:

Cluster sampling has two forms – One-stage & Two-stage cluster sampling. 

In one-stage cluster sampling, a researcher first creates clusters from the participant population, representing the entire population. The clusters are then randomly selected. After which sampling occurs within the selected groups. 

In two-stage cluster sampling, a researcher first randomly selects a group/cluster. The participants for the research are then randomly selected from the selected cluster. 

Simple random sampling selects participants from the total population, making it fundamentally different from both forms of cluster sampling. 

[Read more on Cluster Sampling]

Simple Random Sampling Vs. Systematic Sampling:

In systematic sampling, the researcher assigns all participants a number. A random starting point is determined, and an interval is set to select the first participant. 

If the interval number chosen is 5, then every 5th participant is selected – 5th, 10th, 15th, 20th, 25th…, etc. 

In systematic sampling, since the first participant is randomly selected, the rest of the sample is also considered random. 

Simple random sampling does not have any defined starting point.

In this sampling method, the selection of participants can start from anywhere across the entire population. 

[Read more on Systematic Sampling]

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How is Simple Random Sampling different from other Probability Sampling?

Probability sampling refers to selecting participants or samples from the target population for research. 

Simple random sampling is a process in which a number is assigned to every participant within the population. Then participants for the research are picked in random order. 

This process gives every participant an equal probability of being selected.

There are four types of probability sampling: 

  • Cluster Sampling
  • Systematic Sampling
  • Stratified Sampling 
  • Simple Random Sampling

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Best-in-class ROI

Voxco’s platform helps you gather omnichannel feedback, measure sentiment, uncover insights and act on them.

Join 500 + global clients across 40+ countries