Simple Random Sampling: Definition and Examples

What is simple random sampling?

Definition: Simple random sampling is defined as a sampling technique where every item in the population has an even chance and likelihood of being selected in the sample. Here the selection of items entirely depends on luck or probability, and therefore this sampling technique is also sometimes known as a method of chances. 

Simple random sampling is a fundamental sampling method and can easily be a component of a more complex sampling method. The main attribute of this sampling method is that every sample has the same probability of being chosen.

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The sample size in this sampling method should ideally be more than a few hundred so that simple random sampling can be applied appropriately. They say that this method is theoretically simple to understand but difficult to implement practically. Working with large sample size isn’t an easy task, and it can sometimes be a challenge to finding a realistic sampling frame.

Simple random sampling methods

Researchers follow these methods to select a simple random sample:

  1. They prepare a list of all the population members initially, and then each member is marked with a specific number ( for example, there are nth members, then they will be numbered from 1 to N).
  2. From this population, researchers choose random samples using two ways: random number tables and random number generator software. Researchers prefer a random number generator software, as no human interference is necessary to generate samples.

Two approaches aim to minimize any biases in the process of simple random sampling:

  • Method of lottery

Using the lottery method is one of the oldest ways and is a mechanical example of random sampling. In this method, the researcher gives each member of the population a number. Researchers draw numbers from the box randomly to choose samples.

  • Use of random numbers

The use of random numbers is an alternative method that also involves numbering the population. The use of a number table similar to the one below can help with this sampling technique.

simple random sampling

Simple random sampling formula

Consider a hospital has 1000 staff members, and they need to allocate a night shift to 100 members. All their names will be put in a bucket to be randomly selected. Since each person has an equal chance of being selected, and since we know the population size (N) and sample size (n), the calculation can be as follows:

P=1- N-1/N.N-2/N-1….N-n/N-(n-1)

Cancelling=1-N-n/N

=n/N

 =100/1000

=10%

Example of simple random sampling

Follow these steps to extract a simple random sample of 100 employees out of 500.

  1. Make a list of all the employees working in the organization. (as mentioned above there are 500 employees in the organization, the record must contain 500 names).
  2. Assign a sequential number to each employee (1,2,3…n). This is your sampling frame (the list from which you draw your simple random sample).
  3. Figure out what your sample size is going to be. (In this case, the sample size is 100).
  4. Use a random number generator to select the sample, using your sampling frame (population size) from Step 2 and your sample size from Step 3. For example, if your sample size is 100 and your population is 500, generate 100 random numbers between 1 and 500.

Simple random sampling in research

Today’s market research projects are much larger and involve an indefinite number of items. It is practically impossible to study every member of the population’s thought process and derive interference from the study.

If, as a researcher, you want to save your time and money, simple random sampling is one of the best probability sampling methods that you can use. Getting data from a sample is more advisable and practical

Whether to use a census or a sample depends on several factors, such as the type of census, the degree of homogeneity/heterogeneity, costs, time, feasibility to study, the degree of accuracy needed, etc.

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Advantages of simple random sampling

  1. It is a fair method of sampling, and if applied appropriately, it helps to reduce any bias involved compared to any other sampling method involved.
  2. Since it involves a large sample frame, it is usually easy to pick a smaller sample size from the existing larger population.
  3. The person conducting the research doesn’t need to have prior knowledge of the data he/ she is collecting. One can ask a question to gather the researcher need not be a subject expert.
  4. This sampling method is a fundamental method of collecting the data. You don’t need any technical knowledge. You only require essential listening and recording skills.
  5. Since the population size is vast in this type of sampling method, there is no restriction on the sample size that the researcher needs to create. From a larger population, you can get a small sample quite quickly.
  6. The data collected through this sampling method is well informed; more the samples better is the quality of the data.