The complete probability sampling guide: Definition, types, steps & tips

Probability sampling gives each member of a population an equal chance of being selected. This type of sampling can give you accurate, unbiased research results. Keep reading to learn how probability sampling works and when to use it. We’ll also help you understand the distinctions between probability and non-probability sampling.

Probability sampling is a sampling method that randomly selects a small group (a sample) from a larger population. Then, researchers predict the likelihood that their responses will match those of the population.

Say you wanted to gauge consumer’s reception of your brand’s expansion into the Southeast. You can’t reasonably survey everyone in the region. The sample size would be unmanageable. Probability sampling allows you to survey a smaller group to understand a population.

Probability sampling is a sampling method where everyone in a population has an equal and known chance of being chosen. Random selection ensures that the sample accurately reflects the population’s diversity. This approach minimizes selection bias and allows researchers to make statistical inferences about a population.

Successful probability sampling has three requirements.

With the right sample, you can achieve results that are just as valuable as those you might get from a far bigger survey effort. From there, you can make valid conclusions about the sample's preferences and take actions that fit the whole population.

Probability sampling is ideal for quantitative studies where the goal is to draw conclusions about a large population. Researchers use this sampling strategy to collect representative data when it’s too difficult or expensive to survey a population.

For example, a national coffee shop chain is expanding its customer loyalty program. Before making significant updates, it must conduct market research to learn how customers will respond. However, contacting all customers to do concept testing isn’t feasible.

Using a probability sampling approach, the company can identify a representative sample of its customer base. Different types of sampling methods, such as stratified or cluster sampling, can be used to ensure the sample reflects subgroups.

The responses from the representative sample will accurately represent the larger population. In turn, the coffee shop chain product development team can create a customer loyalty program that customers want. The marketing team can accurately position the program on the market.

Given the expansive primary research use cases, several types of sampling exist to meet diverse objectives. The probability sampling methods are simple random, stratified random, cluster, and systematic sampling.

The key things to know about simple random sampling include:

Many populations can be divided into non-overlapping groups based on characteristics that together represent the whole population. This typically leads to more accurate results than simple random sampling.

Essentials to understand about stratified random sampling include:

Like stratified sampling, cluster sampling separates the population into subgroups or clusters. But that’s where the two probability sampling methods diverge. 

With cluster sampling:

Researchers use cluster sampling to save costs surveying large or geographically spread out populations. However, cluster sampling has a higher risk of sampling error. Each cluster is supposed to represent the total population, but this can be difficult to guarantee.

Systematic sampling, also known as interval sampling, is similar to simple random sampling. 

In systematic sampling:

Systematic sampling is simpler than other methods because it has a clear selection process without a random number generator. On the flip side, the resulting selection may not be as random as if a generator was used. 

For instance, say you plan to survey employees within an organization, and the employees are listed in alphabetically. You use systematic sampling to select every 4th employee for your sample. However, suppose the list is also organized by team and seniority. You might select too many or too few senior people, leading to bias in your sample.

Sampling design is essential to actionable research. Aligning your research goals and sampling method can ensure your sample accurately generalizes your target population.

Consider the following when deciding between sampling methods:

Researchers with fewer resources or less time for their research may need to rely on non-probability sampling. Let’s explore that option.

Simple random sampling, stratified sampling, cluster sampling, and systematic sampling are all types of probability sampling. But there’s another end of the sampling technique spectrum: non-probability sampling. 

Researchers use non-probability sampling for exploratory and qualitative research. The target population are often people with specific expertise, experiences, or insights.

This sampling method has a higher risk of bias than probability sampling as the sample is not random. Members of a population do not have an equal chance of being included in the sample. In fact, some members will have zero chance of being selected. However, the sample size and the results don’t have to represent the population because of its use case.

What’s the difference between probability and non-probability sampling?

It can be hard to get people to respond to a probability survey if they're uninterested or expect compensation. Probability sampling can also be time-consuming without tools to find and randomly select respondents.

Many of these problems can be solved with non-probability sampling. It draws from probability and sampling theory to select an appropriate survey sample.

Researchers have several options when it comes to non-probability sampling.

There are several benefits to using probability sampling. 

In particular, simple random and systematic sampling make implementation more user-friendly, and you can be as detailed as you want when creating population samples.

Stratified sampling reduces researcher bias; cluster sampling limits study variability. These two are also useful when researchers are on deadline.

Each approach has a pitfall that might work against your overall efforts.

So, what are the steps involved in probability sampling? It’s not actually that complicated, but you will need to have clear goals. Pre-planning your study and knowing the results you want will help you decide how to build your sample and why.

Think through all the people that you’re interested in surveying. Also, be aware of anyone who should be deliberately excluded.

Ideally, your frame should include all members of your interested population and no one who is not.

Do you want clusters and strata? Do you want all sample members to have equal probabilities of selection? Think about what makes sense for your area of study, your population members, and your resources.

Depending on the target population, you might have a hard time finding an appropriate sample frame. Even with a good frame, deciding the best selection may force you to make trade-offs among cost, quality, and timeliness.

Now that you understand the steps of probability sampling, you may want to implement random selection using common tools. Learn how to create a random sample using Excel to quickly and effectively select participants from your population while maintaining proper randomization principles.

Effective sampling relies on researchers taking the time to improve their methods and heed best practices.

In particular, with probability sampling, everyone must have an equal opportunity for selection. To avoid knowingly excluding someone from your sample, you might watch out for choices that prevent groups from participating.

For example, let’s say you want to understand public opinion on an expansive new immigration law. If you don't offer a Spanish version of your survey, you unintentionally exclude Spanish speakers. Their perspective is valuable, and without their participation, your results won’t reflect true public opinion.

Along with ensuring you include key segments, you might need to increase your sample size. A larger sample can improve the accuracy and representativeness of the results.

Another way to get a larger sample? Take steps to minimize non-response. This might be follow-ups or incentives. You might also use marketing survey templates to increase the chances that people will respond to the survey.

You can also improve your sampling process by using diverse survey question types to get thoughtful insights from participants.

Pretesting with pilot studies can identify issues that might interfere with participation or result accuracy, reliability, and generalizations.

Technological tools can help you ensure true selection randomness to prevent hidden biases from influencing the sample. You might also use an online survey panel to help you reach a random sample.

Probability sampling can help you draw conclusions about your target population. However, it takes careful consideration and sometimes a lot of resources to find the right people for your research. If you need help from experts, SurveyMonkey Audience can help you reach the right people quickly.