What is Probability Sampling? Pros, Cons, and Examples

Probability sampling is a research method in which every member of a population has a known and equal chance of being selected. This randomization reduces bias and increases the likelihood of obtaining a representative sample, making results more accurate and generalizable. Common methods include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. Probability sampling supports strong statistical analysis and helps researchers make valid inferences about larger populations. While highly reliable, it can require a complete sampling frame, more time, and greater resources. Despite these limitations, probability sampling remains the gold standard for studies requiring accurate, unbiased, and trustworthy data.

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Data that skews one way or another can lead to bad decisions and incorrect conclusions. Often, data is unreliable because researchers who are unable to survey every member of a particular population select a subset of that population that doesn’t represent the group as a whole. However, by employing probability sampling, researchers have the best chance of obtaining a representative sample. Probability sampling enables researchers to collect accurate data that is representative of the population, which is essential for making valid inferences.

Here’s how it works, different methods of probability sampling, how it differs from non-probability sampling, and more. Using probability sampling allows researchers to draw conclusions that are more likely to reflect the true characteristics of the population.

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Introduction to Probability Sampling

Probability sampling is a foundational research technique that enables researchers to select a representative sample from a larger population. By ensuring that every member of the population has a known and equal chance of being selected, probability sampling minimizes bias and increases the likelihood that the sample accurately reflects the characteristics of the entire group. This equal chance of selection is crucial for drawing valid conclusions and making reliable statistical inferences about the larger population. Whether you’re conducting market research, social science studies, or healthcare surveys, using probability sampling methods helps guarantee that each member of the population has a fair chance of being included, resulting in a truly representative sample and more trustworthy results.

Probability Sampling Definition

What does probability sampling mean? Probability sampling is one of many sampling methods. It aims to ensure that everyone in a population has an opportunity to be part of a sample. To achieve this, a subset of the population is selected at random. A probability sample is a group chosen through a random selection process, ensuring each member of the population has a known and equal chance of being included. This is the opposite of non-probability sampling, in which the researcher has some amount of control over who is selected, meaning that not everyone will have a chance of being surveyed. Non-probability sampling methods, such as convenience, quota sampling, and snowball sampling, are often used when it is impractical to use random selection, but they may introduce bias and limit the generalizability of results.

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To better understand the difference between probability sampling vs non-probability sampling, consider a store owner with 5,000 customers. She decides to survey them about their shopping experience. Since surveying 5,000 people is too costly and time-consuming, she decides to survey 10% of them.

With probability sampling, which involves giving everyone within the sample an equal chance of being selected, she uses a number generator (1–5,000) to select 500 customers at random from her customer database that correspond with the numbers generated. These 500 customers make up the sample population.

With non-probability sampling (read more here), she doesn’t care that everyone has an equal chance of being chosen so long as she surveys 500 of them. So, she stands outside her store and surveys each customer who stops by until she reaches 500. She could also send an online survey to the first 500 customers in her database. In either situation, 4,500 people did not have the chance to be surveyed because the selection was not random.

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Probability Sampling Methods

How many types of probability sampling are there? Generally, a researcher will select one of four probability sampling techniques. In the following sections, we will provide probability sampling examples to illustrate how each method works in practice.

1. Simple Random Sampling

As implied by its name, simple random sampling is the easiest way of randomly sampling from a population (also known as obtaining a simple random sample). All the researcher needs to do is assign numbers to everyone in the sample and then randomly choose those numbers; researchers can use random number generators to ensure the selection is unbiased. To be sure the numbers are chosen at random, an automated process is typically employed, which involves randomly selecting numbers—this method randomly selects individuals from the population. The numbers chosen, which represent members of the population, are then surveyed. Randomly selecting participants in this way helps create an accurate sample that reflects the characteristics of the whole group.

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2. Systematic Sampling

Rather than just selecting numbers willy-nilly, systematic sampling draws a random sample from the target population by selecting units at regular intervals starting from a random point (this is known as the systematic sampling approach). This is known as choosing every “nth” (e.g., 5th, 10th, 12th, etc.) individual. This fixed, periodic “nth” is known as the sampling interval.

To proceed with this probability sampling technique, the researcher will use the systematic sampling method: divide their population size by the desired sample size to determine the interval. Then, the researcher will choose a starting point by selecting a number between 0 and the interval. The selection of the next units from other intervals depends upon the position of the unit selected in the first interval. Sound confusing? It’s easier than it may seem. Read more and see an example in our blog on Systematic Sampling.

3. Cluster Sampling

This sampling method is all about dividing the target population into groups or “clusters.” Next, a subsection of each group is randomly selected. Cluster sampling is typically employed when a researcher is studying a large and geographically spread-out population that happens to share similarities (e.g., number of children, occupation, or college major).

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Three types of cluster sampling exist:

• Single-stage cluster sampling, which involves dividing the entire population into clusters and then randomly selecting entire clusters for study, e.g., selecting cities in market research or accountants within the United States.

• Double or two-stage cluster sampling, which goes a step further to narrow the scope. They could use simple random sampling or systematic sampling in this scenario, e.g., every “nth” accountant within the United States.

• Multistage cluster sampling, which involves dividing the cluster into more clusters as a way to narrow down the sample size, e.g., every “nth” accountant within the United States who graduated from a top-tier university.

For example, in environmental research, cluster sampling can be used to select sampling points along a river to assess pollution levels, ensuring comprehensive and unbiased data collection.

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4. Stratified Random Sampling

Last but not least, stratified random sampling involves dividing a large population into smaller groups (also known as stratified sampling) that typically don’t overlap but represent the entire population. Often, this means classifying groups by demographic factors such as gender, age, race, ethnicity, and so on.

The researcher splits subjects into mutually exclusive demographic groups and then uses simple random sampling to select members. When simple random sampling is applied within each stratum, this is sometimes referred to as simple random sampling stratified. These members need to be distinct so that each gets an equal opportunity to be chosen; for that reason, this method of sampling is sometimes referred to as “random quota sampling.”

Stratified or cluster sampling are both effective probability sampling methods for ensuring representation of key subgroups in large populations.

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5. Multistage Sampling

Multistage sampling is a versatile probability sampling method that combines two or more sampling techniques to efficiently select a representative sample from large or complex populations. This approach often begins by dividing the population into clusters, similar to cluster sampling. Next, a random sample of clusters is selected. Within each chosen cluster, researchers then use another probability sampling method—such as simple random sampling or systematic sampling—to select individual elements for the final sample. By layering different sampling methods, multistage sampling allows researchers to manage large-scale studies more effectively, reduce costs, and still achieve a random sample that accurately represents the target population. This method is especially useful when dealing with geographically dispersed populations or when a single-stage sampling method would be impractical.

Probability Sampling Randomization Methods

Randomization is very important in probability sampling. A well-defined sampling strategy is essential to collect data that is representative and reliable. Here are the top three ways to ensure a sample is random. These randomization methods are commonly used to select a survey sample in research studies.

Lottery Method

While it may not seem very scientific, the lottery sampling method is most definitely random. It involves simply writing names (or corresponding numbers) on a piece of paper and placing them into a hat, fishbowl, shoebox, or other holder of the researcher’s choice, and then selecting them at random. All participants have an equal chance of being selected, and personal preference cannot be factored in. Of course, this method is really only feasible when the total population is small (e.g., students in a classroom, employees in a department, and so on).

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Number Generator

When sample sizes are large, many researchers use computer-aided simple random sampling methods such as the random number generator available on Calculator.net, noting that random number generators are widely used tools for ensuring unbiased selection in probability sampling. For example, a researcher has a population of 5,000 but can only afford to sample 100 of them. They simply adjust the limits and click “Generate” to reveal a random number (in the screenshot below, it’s 1513). They do this 100 times to determine the sample members.

Microsoft Excel RAND Function

Most researchers house their data in a Microsoft Excel spreadsheet, making randomization easy. Again, a researcher has a database of 5,000 and wants a sample of 100. By typing in the formula =RAND() and then pressing enter, Excel assigns a random number to each name on the list. Check out the video below for more on the RAND function.

Advantages of Probability Sampling

Probability sampling pros make it a great research method. For one, it generally offers results that are representative of the target population. Unlike forms of non-probability sampling (such as convenience sampling, in which researchers just select participants that are easily accessible to them), probability sampling is completely random. That means that every participant has an equal chance of being part of the selection. Using a larger sample can help minimize sampling error and improve the reliability and accuracy of the results.

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Note: Though unlikely, it is possible for a random sample to result in skewed data. For example, if a researcher pulled 20 names from 100 out of a hat and all 20 happened to be men. To avoid this, the researcher could place 50 men’s names in one hat and 50 women’s names in another, and select 10 from each. Statistical analysis can be used to assess whether the sample is truly representative of the whole population.

In addition, unlike non-probability sampling, in which the potential for sampling bias is high (e.g., a researcher choosing people he or she is comfortable with or who fall into a certain demographic), this isn’t true of probability sampling. By choosing participants at random, a researcher’s personal views and opinions cannot influence the sample. Probability sampling helps ensure the sample is representative and allows researchers to estimate and control sampling error.

Limitations of Probability Sampling

Despite its many advantages, probability sampling does come with certain limitations. One significant challenge is the need for a comprehensive sampling frame—a complete list of all members of the population—which can be both time-consuming and costly to compile, especially for large or hard-to-reach groups. Additionally, even with a well-designed sampling process, researchers may encounter non-response bias if certain members of the population are less likely to participate, potentially affecting the accuracy of the collected data. Another limitation is related to sample size; if the sample is too small, it may not capture the full diversity of the population, leading to less reliable results. These factors highlight the importance of careful planning and resource allocation when using probability sampling methods to ensure the most accurate and representative outcomes.

Probability Sampling with SurveyLegend Online Surveys

Online surveys are a great way to conduct probability sampling. Conducting a probability survey online allows researchers to reach a diverse and representative sample efficiently. They are convenient for respondents, who can complete them at the time and place of their choice, and cost-efficient for the researcher because of reduced interview costs and the ability to easily reach across geographical boundaries.

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With SurveyLegend, our online surveys are easy to create and easy on the eyes – that’s because you can add pictures to surveys, boosting engagement, triggering respondent emotion and memory, and crossing language barriers. Below is an example of one of our photo surveys, designed to match the small business survey we highlighted at the start of this blog. You’ll note that multiple types of survey questions are used to engage participants, including a picture question, opinion scale, and a rating system.

Conclusions

Probability sampling is a great way for researchers to scientifically survey a small population or a smaller subset of a large population. When researchers use randomization to determine sample groups, there’s no room for researcher bias and sampling bias. This results in data that is typically very reliable. Whether you choose this type of sampling or another technique, SurveyLegend has you covered. We let you start for free, and have dozens of beautiful and responsive online survey templates from which to choose.

Do you use probability sampling when surveying? If so, what randomization technique do you use? Let us know in the comments!

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