What Is Simple Random Sampling?  Example & Definition
In simple random sampling, every member of the population has an equal probability of being chosen for the sample.
This probability sampling method is the easiest to execute because it requires minimal prior knowledge about the population and it involves only one random selection.
The use of randomization ensures that the sample is representative of the population, with a reduced risk of biases such as sampling bias and selection bias. Additionally, the sample’s internal and external validity are likely to be high.
What is simple random sampling?
Simple random sampling is a method for making statistical inferences about a population. By introducing randomness, it effectively minimizes the influence of potential confounding variables, thereby ensuring high internal validity.
When a sufficiently large sample size is obtained, a simple random sample can accurately represent the characteristics of the larger population, which means the sample has high external validity.
Implementing simple random sampling can be complex. To utilize this method, your research needs to meet several criteria:
 A complete list of the population’s members is available or can be created.
 Each selected member can be contacted or accessed.
 Sufficient time and resources are available to collect data from the required sample size.
Advantages of simple random sampling
Simple random sampling comes with important advantages.
 Ease of implementation. Simple random sampling is relatively easy to implement. You don’t have to group your population into strata or clusters before randomly drawing a sample from the list.
 Representative sample. When done correctly, simple random sampling provides you with a representative sample of the population. Due to randomization, each subject has an equal chance of being selected.
 Lack of bias. An additional benefit of randomization is the minimized risk of research biases. Researchers can’t influence the selection process because all subjects are chosen at random by, for example, a random number generator.
Disadvantages of simple random sampling
Simple random sampling also comes with some (mostly practical) disadvantages.
 Difficulty of obtaining a list of the entire population. It’s typically difficult to obtain a full list of the entire population you’re interested in. All subjects who don’t end up on the population list have no chance of ending up in the final sample.
 Limited flexibility. Simple random sampling is a fixedprobability sampling method, so you can’t make changes during the sampling process.
 Requirement of a large sample size. Simple random sampling generally requires a large sample to achieve high accuracy and precision levels. This can be costly and timeconsuming.
Other types of sampling
Simple random sampling is most effective when examining a limited population that can be easily sampled or when conducting a study with ample time and resources.
In certain situations, it may be better to use a different probability sampling method:
 Cluster sampling is a good choice when it is not feasible to sample from the entire population. In this case, you can divide the sample into clusters that approximate the characteristics of the larger population and then randomly select from a subset of these clusters.
 Systematic sampling involves sampling based on a predetermined, regular interval, rather than relying solely on random chance. This approach can be particularly useful when a complete list of the population is not available.
 Stratified sampling is a good method when you want specific characteristics to be proportionally represented in the sample. This involves dividing the population into subgroups, known as strata, based on relevant characteristics (e.g., gender identity or age) and then randomly selecting from each of these strata.
Simple random sampling example
Simple random sampling consists of four steps:
 Define the population
 Decide on the sample size
 Randomly select the sample
 Collect data from the sample
Step 1: Define the population
First, you need to decide which population you’re interested in. It’s important that you obtain an exhaustive list of members of the population and that you have access to all of them for data collection.
Step 2: Decide on the sample size
There are multiple ways to calculate the required sample size, but the easiest way is by using a free sample size calculator.
Sample size calculators generally use a simple formula with your chosen confidence level and margin of error, as well as the estimated size of the population and the approximate standard deviation of what you want to measure.
Most researchers choose 0.95 for their confidence level and 0.05 for their margin of error (or confidence interval). In cases where the standard deviation is unknown, you should make sure to choose a high enough number to account for most possibilities.
Step 3: Randomly select your sample
You can use two methods to randomly select your sample:
 The random number method. You assign a number to every member of the population. You use a random number generator to randomly draw a sample. Another way of randomly drawing a sample is by using the random number function (RAND) in Microsoft Excel.
 The lottery method. You randomly select a sample by drawing subjects from a hat (or a digital version of that action).
Step 4: Collect data from your sample
In the final step, you collect data from every member of your sample.
It’s important to ensure that every member of your sample actually takes part in the research. If some of them are unwilling to participate or drop out of the study because of the topic or design, this could lead to bias in your results.
For example, if women are less likely to participate in your study than people of a different gender, they might be underrepresented in your study. This can harm the external validity of your results.
Frequently asked questions about simple random sampling
 Why is systematic random sampling sometimes used in place of simple random sampling?

Systematic sampling is sometimes used in place of simple random sampling because it’s easier to implement.
With systematic sampling, you only draw one random number and then select subjects at regular intervals. This is especially helpful when the population is large.
 What are the pros and cons of simple random sampling?

Simple random sampling is one of the most commonly used probability sampling methods.
The most important pros of simple random sampling are:
 Ease of implementation. This method is relatively easy to implement. You don’t have to think about strata (like with stratified sampling) or clusters (like with cluster sampling).
 Representative sample. Simple random sampling provides a representative sample of the population, with each unit having an equal chance of being selected.
 Lack of bias. Because of the random nature of this technique, the risk of research biases is minimized. Researchers can’t influence the selection process.
The most important cons of simple random sampling are:
 Limited flexibility. This sampling method is a fixedprobability sampling method, which means it can’t be adapted to changing circumstances during the sampling process.
 Requirement of a large sample size. This technique typically requires large sample sizes to achieve acceptable levels of precision and accuracy, which can be expensive and timeconsuming.
 Difficulty of obtaining a list of entire population. It can be very difficult to obtain an exhaustive list of the entire population. This means some individuals who should be on the list have no chance of ending up in the sample.
 Is simple random sampling probability or nonprobability sampling?

Simple random sampling is a common probability sampling technique.
In probability sampling, each individual in the population has the same chance of being selected for the sample. With simple random sampling, individuals are chosen from a list at random, which makes it a probability sampling method.
Other examples of probability sampling are stratified sampling, systematic sampling, and cluster sampling. Examples of nonprobability sampling are convenience sampling, quota sampling, selfselection sampling, snowball sampling, and purposive sampling.