Simple Random Sampling: Definition, Advantages, and Disadvantages

Reviewed by Robert C. KellyFact checked by Vikki VelasquezReviewed by Robert C. KellyFact checked by Vikki Velasquez

What Is Simple Random Sampling?

Simple random sampling (SRS) is a technique in which a researcher selects a random subset of people from a larger group or population. In simple random sampling, each member of the group has an equal chance of getting selected. The method is commonly used in statistics, a branch of applied mathematics, to obtain a sample that is representative of the larger population.

The simple random sampling method allows researchers to statistically measure a subset of individuals selected from a larger group or population to approximate a response from the entire group. Put simply, you can make inferences about a large population by examining a smaller sample.

This research method is commonly used for statistical analysis used to identify trends in many different areas, including business and finance. Individuals can use findings from statistical research to make better decisions about their money, businesses, and investments.

SRS has both benefits and drawbacks. We highlight these pros and cons in this article, along with an overview of simple random sampling.

Key Takeaways

• A simple random sample is one of the methods researchers use to choose a sample from a larger population.
• This method works if there is an equal chance that any of the subjects in a population will be chosen.
• Researchers choose simple random sampling to make generalizations about a population.
• Major advantages include its simplicity and lack of bias.
• Among the disadvantages are difficulty gaining access to a list of a larger population, time, costs, and that bias can still occur under certain circumstances.

Understanding Simple Random Sampling

As noted above, simple random sampling involves choosing a smaller subset of a larger population. This is done randomly. But the catch here is that there is an equal chance that any of the samples in the subset will be chosen. Researchers tend to choose this method of sampling when they want to make generalizations about the larger population.

Simple random sampling can be conducted by using:

• The lottery method: this method involves assigning a number to each member of the dataset and then choosing a prescribed set of numbers from those members at random.
• Technology: using software programs like Excel makes it easier to conduct random sampling. Researchers just have to make sure that all the formulas and inputs are correctly laid out.

For simple random sampling to work, researchers must know the total population size. They must also be able to remove all hints of bias as simple random sampling is meant to be a completely unbiased approach to garner responses from a large group.

Keep in mind that there is room for error with random sampling. This is noted by adding a plus or minus variance to the results. In order to avoid any errors, researchers must study the entire population, which for all intents and purposes, isn’t always possible.

Important

To ensure bias does not occur, researchers must acquire responses from an adequate number of respondents, which may not be possible due to time or budget constraints.

Advantages of Simple Random Sampling

Simple random sampling may be simple to perform (as the name suggests) but it isn’t used that often, although that doesn’t mean it shouldn’t be used. As long as it is done properly, there are certain distinct advantages to this sampling method.

Lack of Bias

The use of simple random sampling removes all hints of bias—or at least it should. Because individuals who make up the subset of the larger group are chosen at random, each individual in the large population set has the same probability of being selected. In most cases, this creates a balanced subset that carries the greatest potential for representing the larger group as a whole.

Here’s a simple way to show how a researcher can remove bias when conducting simple random sampling. Let’s say there are 100 bingo balls in a bowl, from which the researcher must choose 10. In order to remove any bias, the individual must close their eyes or look away when choosing the balls.

Simplicity

As its name implies, producing a simple random sample is much less complicated than other methods. There are no special skills involved in using this method, which can result in a fairly reliable outcome. This is in contrast to other sampling methods like stratified random sampling. This method involves dividing larger groups into smaller subgroups that are called strata. Members are divided up into these groups based on any attributes they share. As mentioned, individuals in the subset are selected randomly and there are no additional steps.

Less Knowledge Required

We’ve already established that simple random sampling is a very simple sampling method to execute. But there’s also another, similar benefit: It requires little to no special knowledge. This means that the individual conducting the research doesn’t need to have any information or knowledge about the larger population in order to effectively do their job.

Be sure that the sample subset from the larger group is inclusive enough. A sample that doesn’t adequately reflect the population as a whole will result in a skewed result.

Disadvantages of Simple Random Sampling

Although there are distinct advantages to using a simple random sample, it does come with inherent drawbacks. These disadvantages include the time needed to gather the full list of a specific population, the capital necessary to retrieve and contact that list, and the bias that could occur when the sample set is not large enough to adequately represent the full population. We go into more detail below.

Difficulty Accessing Lists of the Full Population

An accurate statistical measure of a large population can only be obtained in simple random sampling when a full list of the entire population to be studied is available. Think of a list of students at a university or a group of employees at a specific company.

The problem lies in the accessibility of these lists. As such, getting access to the whole list can present challenges. Some universities or colleges may not want to provide a complete list of students or faculty for research. Similarly, specific companies may not be willing or able to hand over information about employee groups due to privacy policies.

Time Consuming

When a full list of a larger population is not available, individuals attempting to conduct simple random sampling must gather information from other sources. If publicly available, smaller subset lists can be used to recreate a full list of a larger population, but this strategy takes time to complete.

Organizations that keep data on students, employees, and individual consumers often impose lengthy retrieval processes that can stall a researcher’s ability to obtain the most accurate information on the entire population set.

Costs

In addition to the time it takes to gather information from various sources, the process may cost a company or individual a substantial amount of capital. Retrieving a full list of a population or smaller subset lists from a third-party data provider may require payment each time data is provided.

If the sample is not large enough to represent the views of the entire population during the first round of simple random sampling, purchasing additional lists or databases to avoid a sampling error can be prohibitive.

Sample Selection Bias

Although simple random sampling is intended to be an unbiased approach to surveying, sample selection bias can occur. When a sample set of the larger population is not inclusive enough, representation of the full population is skewed and requires additional sampling techniques.

Data Quality Is Reliant on Researcher Quality

The success of any sampling method relies on the researcher’s willingness to thoroughly do their job. Someone who isn’t willing to follow the rules or deviates from the task at hand won’t help get a reliable result. For instance, there may be issues if a researcher doesn’t ask the appropriate questions or asks the wrong ones. This could create implicit bias, ending up in a skewed study.

What Is Simple Random Sampling?

The term simple random sampling (SRS) refers to a smaller section of a larger population. There is an equal chance that each member of this section will be chosen. For this reason, a simple random sampling is meant to be unbiased in its representation of the larger group. There is normally room for error with this method, which is indicated by a plus or minus variant. This is known as a sampling error.

How Is Simple Random Sampling Conducted?

Simple random sampling involves the study of a larger population by taking a smaller subset. This subgroup is chosen at random and studied to get the desired result. In order for this sampling method to work, the researcher must know the size of the larger population. The selection of the subset must be unbiased.

What Are the 4 Types of Random Sampling?

There are four types of random sampling. Simple random sampling involves an unbiased study of a smaller subset of a larger population. Stratified random sampling uses smaller groups derived from a larger population that is based on shared characteristics and attributes. Systematic sampling is a method that involves specific members of a larger dataset. These samples are selected based on a random starting point using a fixed, periodic interval. The final type of random sampling is cluster sampling, which takes members of a dataset and places them into clusters based on shared characteristics. Researchers then randomly select clusters to study.

When Is It Best to Use Simple Random Sampling?

It’s always a good idea to use simple random sampling when you have smaller data sets to study. This allows you to produce better results that are more representative of the overall population. Keep in mind that this method requires each member of the larger population is identified and selected individually, which can often be challenging and time consuming.

The Bottom Line

Studying large populations can be very difficult. Getting information from each individual member can be costly and time-consuming. That’s why researchers turn to random sampling to help reach the conclusions they need to make key decisions, whether that means helping provide the services that residents need, making better business decisions, or executing changes in an investor’s portfolio.

Simple random sampling is relatively easy to conduct as long as you remove any and all hints of bias. Doing so means you must have information about each member of the larger population at your disposal before you conduct your research. This can be relatively simple and require very little knowledge. But keep in mind that the process can be costly and it may be hard trying to get access to information about all of the members of the population.

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