Simple random sampling is a statistical method in which everyone in a population has an equal chance of being selected into a sample. The sample represents a smaller and more manageable portion of the people that can be studied and analyzed. It’s a fundamental technique to gather data and make inferences about a population.
Simple random sampling is considered a fair and unbiased sample selection method. This type of sampling is the most straightforward sample selection bias method.
What is Simple Random Sampling?
Simple random sampling is a technique where every item in the population has an even chance and likelihood of being selected. Here, the selection of items entirely depends on luck or probability. Therefore, this sampling technique is also a method of chance.
Simple random sampling is a fundamental method and can easily be a component of a more complex method. The main attribute of this sampling method is that every sample has the same probability of being chosen.
The sample size in simple random sampling method should ideally be more than a few hundred so that it can be applied appropriately. This method is theoretically simple to understand but difficult to implement practically. Working with a large sample size isn’t an easy task, and it can sometimes be challenging to find a realistic sampling bias frame.
Simple Random Sampling Methods
Researchers follow these methods to select a simple random sample:
- They prepare a list of all the population members initially, and each member is marked with a specific number ( for example, if there are nth members, then they will be numbered from 1 to N).
- Researchers from this population choose random samples using random number tables and random number generator software. Researchers prefer random number generator software, as no human interference is necessary to generate samples.
Two approaches aim to minimize any biases in the process of this method:
Method of lottery
Using the lottery method is one of the oldest ways and is a mechanical example of random sample. Researchers draw numbers from the box randomly to choose samples. In this method, the researcher gives each member of the population a number.
Use of random numbers
Using random numbers is an alternative method that also involves numbering the population. A numbered table similar to the one below can help with this sampling technique.
Simple Random Sampling Formula
Consider that a hospital has 1000 staff members and must 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. Since we know the population size (N) and sample size (n), the calculation can be as follows:
Simple Random Sampling Steps
Follow these steps to extract a simple random sample of 100 employees out of 500.
- Make a list of all the employees working in the organization. (as mentioned above, there are 500 employees in the organization, so the record must contain 500 names).
- Assign a sequential number to each employee (1,2,3…n). This is your sampling frame (the list from which you draw your sample).
- Figure out what your sample size is going to be. (In this case, the sample size is 100).
- Use a random number generator to select the sample, using your 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.
Using 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.
LEARN ABOUT: Purposive Sampling
Advantages of Simple Random Sampling
Simple random sampling has several advantages, including:
- It is a fair sampling method, and if applied appropriately, it helps reduce any bias involved compared to any other sampling method.
- Since it involves a large sample frame, it is usually easy to pick a smaller sample size from the existing larger population.
- 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.
- 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.
- 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.
- The data collected through this sampling method is well informative; the more samples better is the quality of the data.
Overall, this is a valuable and versatile method for gathering data and making inferences about populations.
LEARN ABOUT: Survey Sampling
Researchers use simple random sampling in statistical analysis methods valuable for various applications. By selecting a sample of individuals from a population in a random and unbiased manner, it provides a representative sample and cost-effective way of gathering data and making inferences about populations.
With QuestionPro, researchers and data analysts can easily and efficiently implement simple random sampling in their research and studies. We are here to help to ensure that the results are accurate. When a market researcher is looking to gather insights about your target audience or a social scientist is looking to study a population. Simple random sampling with QuestionPro is a reliable and effective method to consider.