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GuidesResearch methodsAn introduction to simple random sampling

An introduction to simple random sampling

Last updated

5 April 2023

Author

Dovetail Editorial Team

Reviewed by

Cathy Heath

Simple random sampling is a statistical technique that gives every member of a population a chance to be chosen for a sample. It’s a subset of a population selected randomly for study and analysis. This method offers both high internal and external validity since it uses randomization.

Simple random sampling is the easiest sample selection approach with the lowest chance of research biases, such as selection and sampling bias. In this article, we'll take you through all you need to know about simple random sampling, including when to use it in your research.

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What is a simple random sample?

As mentioned, a simple random sample is a portion of the statistical population where each person is equally likely to be picked for a study. Thus, it is a fair depiction of a sample because its selection process is based purely on luck.

How are random samples used?

Researchers use simple random samples to generalize a study population without any objective or bias. They also enable researchers to use statistical approaches and make conclusions and predictions about the study population without surveying or collecting data from the whole group.

When to use simple random sampling

Researchers use simple random sampling to derive statistical findings about a population. This sampling method needs a sample size of at least 100 to be useful in a research project and allow you to draw some basic analysis. Although this approach may appear straightforward in theory, it takes time to implement. Simple random sampling can require a complete list of every member of the population, who must be contactable before the research project begins. 

Adequate time and resources are also crucial for collecting data from the required sample size. This statistical approach is useful when you have the time and resources to perform your study.

Finding a feasible sampling frame to specify the population of interest can take work, especially when using an extensive sample size. It’s much easier to pull together with a smaller sample. 

Random sampling techniques

There’s no particular technique for determining the random values in a simple random sample. Researchers can either make an initial list of every member of the population and label each with a unique number or sample the population using random number tables and random number generator software.

To reduce any biases in the sampling process, you can use approaches such as:

  • Random lottery: This is one of the oldest methods and gives a mechanical example of a random sample. In this approach, you can draw numbers from a stored box or container without a view.

  • Physical approach: This is a random selection method that requires using a dice, spinning wheels, or flipping coins. Each outcome is assigned a value or outcome relating to the population.

  • Using random numbers: This can be a sample table with randomized numbers, an online random generator tool, or random numbers from an Excel spreadsheet.

How to perform simple random sampling

It’s essential that you follow the following steps when using a simple random sampling method in your research:

1. Start by defining the population

First, decide on the population you want to research. This ensures that you know and can reach all the group members, allowing you to gather data from everyone chosen for the sample.

2. Choose your sample size

Choosing your sample size will help you determine whether you’ll research a small or large population. Remember that a larger group requires more funding and resources, while a smaller sample can lead to trust issues around statistics certainty.

While there are numerous ways to determine a study sample size, one of the most straightforward involves selecting your preferred confidence interval and confidence level, approximating the population size you'll be working with, and calculating the standard deviation of anything you'd like to measure in the population you’re studying.

How to calculate the confidence interval formula

Once all the estimates are in place, you can use a sample size tool to calculate the required sample size. Most researchers use 0.05 and 0.95 as confidence intervals and levels, respectively. A standard deviation of 0.5 may be suitable because it allows for many possibilities. 

3. Choose your sample

You can achieve this using the above-mentioned random sampling techniques, such as the lottery, physical, and random number approaches. 

4. Gather data from your sample

The final step requires that you collect data from your study sample. Sometimes, study samples fail to participate in the research due to issues with the research question, or they may even drop out of the study, resulting in biased results.

For instance, if your research fails to engage young participants for unknown reasons, the outcomes may be invalid due to the underrepresentation of this group. Therefore, make sure your data originates from all individuals chosen for research to ensure the validity of your results.

What are the four types of random sampling?

The four primary random sampling methods include:

  1. Simple random sampling: Simple random sampling gives all members in the population an equal chance of being picked for the sample. It’s also known as representative sampling because the sample size is large, and the person is randomly selected.

  2. Cluster sampling: Like stratified random sampling, this method divides a group into subclasses, such as location, gender, race, etc. It involves the selection of an entire subclass at random. This method is perfect for studies involving large populations.

  3. Systematic sampling: This sampling entails selecting particular people from a large population, using a set or sequence, such as selecting every fifth person from a list.

  4. Stratified random sampling: The researcher splits the population into small groups based on a certain characteristic (e.g., age or gender). This is known as strata. A sample is then taken from each stratum proportionate to its size in the population.

Simple random vs other sampling methods

Simple random vs stratified random sample

Simple random sampling and stratified random sampling have some notable variations. A simple random sample, for example, reflects the total data population, whereas a stratified sample divides the population into strata based on shared features.

Stratified random samples work with populations that you can readily divide into subgroups or subsets. You form these groups based on specific criteria, and elements from each are chosen randomly in proportion to the size of the group compared to the population.

Simple random vs systematic sampling

Unlike simple random sampling, which has no starting point, systematic sampling involves choosing a single random variable that determines the internal structure of the population items. Also, in simple random sampling, each data point has an equal chance of being chosen, whereas, in systematic sampling, you choose one data point for each specified interval.

Although systematic sampling is less challenging to implement than simple random sampling, it can generate skewed results if the data collection contains patterns. It’s also easier to control.

Simple random vs cluster sampling

Cluster sampling depends on one or more clusters, such as a one-stage or two-stage cluster. You group people within a population into similar categories in a one-stage cluster, followed by a sampling process. Two-stage cluster sampling occurs when clusters are developed randomly rather than by their similarities. After that, the sample is chosen at random.

Simple random sampling, on the other hand, has no clusters or divisions. While simple random sampling is simpler, clustering is far superior because it can improve the randomness of sample selection. Furthermore, clustering can offer a more in-depth analysis of a particular population sample, which may enhance the study results.

What are the advantages and disadvantages of simple random samples?

Simple random samples come with several advantages, including:

  • There is increased fairness compared to other sampling techniques. This is because it minimizes bias.

  • It results in quality data collection because it allows for the collection of accurate data from the sample.

  • It provides a simple method to select a smaller sample size from the entire population because it includes a large sample frame.

  • It requires minimal technical expertise because no special knowledge is required. You only need basic listening and documenting abilities.

  • There’s no limit to the number of samples you can make. You can rapidly pick a small sample from a larger population.

This type of sampling can also have significant drawbacks that make the data collected irrelevant, for example:

  • Sampling errors can occur if the sample doesn’t represent the population it intended to represent appropriately.

  • If you exclude certain groups, you may receive skewed results due to imprecise population demographics.

  • The research results can be tedious and costly to analyze compared to other methods, depending on the size and format of the data collection.

  • It may have a different population representation because the sample was randomly picked.

  • Non-response bias can present inaccurate results if certain groups decide not to complete the research.

The lowdown

Simple random sampling is a statistical analysis technique that results in everyone in a study population having an equal probability of being picked as a sample. It enables the selection of a random and fair sample from an entire population. 

The sampling method begins with generating a list of every member of a population and giving each a sequential number. You then determine the size of your sample and pick people at random. While this method is relatively easy to use, it has some features that need to be considered, such as additional time and resources based on the size and format of the data collection.

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