Probability sampling: definition, types and steps

How do you conduct an accurate national survey when there are some 68 million people living in the United Kingdom? It would be impossible to send a survey to every single person, but you can use probability sampling to get data that’s just as good, even if it comes from a much smaller group.

Probability sampling is a sampling technique that involves randomly selecting a small group of people (a sample) from a larger population and then predicting the likelihood that all their responses put together will match those of the overall population. 

There are two important requirements when it comes to probability sampling:

Following these two rules will help you choose appropriately (i.e. randomly) from your sampling frame, which is the list of everyone in your entire population who can be sampled. Random selection is key: probability sampling is all about making sure everyone has an equal probability of being included. From picking names out of a hat or pulling the short straw, to more complex random selection processes, this ensures that the sample you end up creating is representative of the population as a whole. 

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 draw valid conclusions based on the sample’s wants, needs, or opinions and take action that makes sense for the entire population.

There are several sampling methods that fall under the umbrella of probability sampling. These methods not only vary based on the type of research you’re doing and the type of data you want to yield but also the amount of time you have to conduct your research and the tools you have at your disposal. Here are the four main types of probability sampling approaches that researchers use:

In simple random sampling, all members of the population have an equal chance of being selected and the selection is done randomly. To achieve this, researchers may use tools such as a random number generator to select participants from the overall population to be part of a sample. However, although simple random sampling is, as the name indicates, the simplest sampling strategy, it is also prone to bias. For example, the smaller your sample size is compared to your overall population, the less likely you are to draw a reliable sample totally at random. 

SurveyMonkey Audience can help you tap into a true representative sample with demographic balancing and flexible targeting.

Many populations can be divided into smaller groups based on specific characteristics that don’t overlap but represent the entire population when put together. With stratified random sampling, you would draw a sample from each of these groups (or strata) separately. This allows you to ensure that every subgroup is properly represented, which leads to more accurate results than simple random sampling.

It’s common to stratify according to characteristics such as gender, age, income bracket or ethnicity. The strata must be specific and mutually exclusive, meaning that every individual in the population should only be assigned to one group. Once you’ve split your population into strata, you would then use simple random sampling to select individuals from each group, in proportion to the total population. Those individuals would then be combined into a single sample.

Like stratified sampling, cluster sampling also involves separating the population into subgroups, or clusters. But that’s where the two probability sampling methods diverge. With cluster sampling, each cluster should have similar characteristics to the population. Instead of selecting individuals from each and every cluster, you would begin by randomly selecting entire clusters. If possible, you might include every individual from each selected cluster in your final sample. If the clusters are too large, you would need to randomly select individuals from each cluster. 

Researchers often use pre-established and easily available groups as clusters. This is typically based on geographic boundaries, such as cities or counties, but it could also be educational institutions or office locations. Cluster sampling is most often used to save costs when surveying populations that are very large or spread out geographically. However, there is more risk of sampling error with cluster sampling. Each cluster is supposed to represent the total population, but this can be difficult to guarantee. 

Systematic sampling is similar to simple random sampling, although it’s usually a bit easier to conduct. Each member of the population is assigned a number and then selected at regular intervals to form a sample. (Systematic sampling is also known as interval sampling.) Or, to put it another way, every ‘nth’ individual in the population is selected to be part of the sample.

For example, in a population of 1,000, you might choose every ninth person for your sample. This can be more straightforward than other sampling methods, as there is a clear and systematic approach to picking individuals that doesn’t involve a random number generator. On the flip side, the resulting selection may not be as random as it would be if a generator was used. Additionally, it’s important to ensure that there’s no hidden pattern in the list that may affect the random selection. If there’s risk of data manipulation, the sample will be skewed and you may end up with over- or under-representation within your sample. 

For instance, let’s suppose you plan to survey employees within a particular organisation and all the employees are listed in alphabetical order. You plan to use systematic sampling to select every fourth employee for your sample. However, if the alphabetical list is also organised by team and seniority, you might end up choosing too many or too few people in senior roles, which would lead to bias in your sample.

There are several benefits associated with using probability sampling. Overall, it’s cost-effective to sample large audiences representing your target buying audience. It’s also advantageous for geographically dispersed populations. 

Each type of probability sampling provides its own advantages. For example, simple random and systematic sampling makes the implementation process more user-friendly and stratified sampling reduces the researcher’s bias, while cluster sampling limits the variability in a research study. Probability sampling requires little technical expertise when utilising an agile experience management platform. You can also be as detailed as you want when creating your population sample using stratified sampling or systematic sampling. If you’re working to deadlines, then cluster sampling and simple random sampling is the way to go. 

For every advantage, some detail within that benefit might work against your overall efforts. For instance, getting the best-possible population sample means doing a little more research that will take more time and resources. Stratified sampling can ensure that the clusters are equally represented, but it may not mirror all the differences within that sample population. 

Cluster sampling can separate the strata into diverse clusters, but those clusters could have overlapping characteristics. While simple random sampling and probability sampling can provide quick results, the clusters and strata might not be as targeted towards your intended audience. 

Probability sampling is ideal for quantitative studies where the goal is to use statistical analysis to draw conclusions about a large population. When it would be too difficult or expensive to survey the entire population, researchers can use this sampling strategy to collect representative data.

Probability sampling is used in a lot of market research to gain insights into a large population. This includes projects such as: 

Even beyond industry tracking, buyer attitudes and competitive intelligence, probability sampling allows companies to firm up new ideas and improve business by tapping into data that reflects their entire target market. 

Let’s suppose, for example, that a chain of coffee shops has 10,000 shops in various geographic locations across the United Kingdom. The company is looking to expand its customer loyalty programme with additional payment options and new ways for customers to earn rewards. Before it makes any significant updates, however, it wants to find out whether customers will respond well to the proposed changes. 

Reaching out to all the customers at its 10,000 coffee shops isn’t feasible, but the company could use a probability sampling approach to create a sample that accurately represents that larger population. The responses received will reveal how customers as a whole feel about the loyalty programme update. In turn, everyone from the company’s marketing department to its customer service representatives can use the data to gain a better understanding of what further changes need to be made or how to effectively promote the new loyalty programme. And if the company wants to ensure that its sample reflects subgroups within the population, such as gender, age ranges or income levels, it can use certain types of probability sampling methods such AS stratified sampling or cluster sampling. 

In the example above, probability sampling is a great way to handle a rather large population: in this case, thousands of coffee shops. With true probability samples, having larger samples helps reduce the chance of sampling error, which occurs when you select a sample that does not represent the whole population. And, in general, random sampling can help minimise sampling errors because it uses a systematic, rather than subjective, approach to selecting a sample.

You never want to knowingly exclude someone in your population from being selected to be part of your sample. Watch out for times when particular groups might be unintentionally prevented from participating.

For example, let’s suppose you want to understand public opinion on an expansive new immigration law. Will you offer a Romanian-language version of your survey? Well, you should. If you don’t, it’s likely that you will miss out on hearing from a lot of native Romanian speakers who aren’t comfortable answering questions in English but have views on immigration that would be extremely valuable for your research. If their participation is overlooked, your survey results won’t reflect true public opinion.

Remember, if you can’t give everyone in your population a chance to complete your survey, your sample will be non-representative and, therefore, will not be based on probability sampling.

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. Even if you’re dead set on using random selection for your sample, it’s worth knowing the basics of non-probability sampling, including when and why it’s used by researchers. 

With non-probability sampling, members of the overall population do not have an equal chance of being part of your sample, and there’s nothing random about how they are selected. In fact, some members will have zero chance of being selected. Where probability sampling is concerned with drawing conclusions about a larger population, non-probability sampling is often used for exploratory and qualitative research that is more focused on hearing from people with specific expertise, experiences or insights. 

Let’s suppose, for example, that you’re researching local use of mobility ramps and your population of interest is people in your city who use wheelchairs. You don’t have a full list of these people, so probability sampling isn’t an option. However, you meet a few people who agree to participate in your study and they connect you with other wheelchair users in the area. Although this non-probability sampling, called snowball sampling, may not involve random selection, it does have the potential to put you in contact with more people who are relevant to your research.  

Non-probability sampling is generally easier and cheaper to conduct, but it also has a higher risk of sampling bias than probability sampling. That’s because the sample selection process is based on the subjective judgement of the researcher rather than randomisation. Plus, the sample size and the end results don’t necessarily have to represent the entire population. 

So what are the steps involved in probability sampling? It’s not actually that complicated, but you will need to have clear goals and interests for your study. Pre-planning, and having a thorough understanding of what kind of results you hope to attain, will be extremely helpful when you need to narrow down how you plan to build your sample and why. 

Think through all the people that you’re interested in hearing from, but also be aware of anyone who should be deliberately excluded.

Ideally, your frame should include all members of your population of interest (and no one who is not in your population of interest).

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

Depending on the population you’re trying to survey, you might have a hard time finding an appropriate sample frame. Even if you have a good frame, deciding on the best selection strategy may force you to make trade-offs between cost, representation, quality and timeliness.

Getting people to respond to a true probability survey can be difficult if they are uninterested in the survey topic or want to be compensated for the time and effort it takes to complete the survey. It can also be time-consuming. For example, if you’re conducting market research on your own (without the use of tools that help you find and randomly select respondents), creating a larger sample might require a lot of time and effort, and that’s before you get to the analysis portion of your research. 

Many of these problems can be solved with non-probability sampling, which (despite its name) still draws from probability and sampling theory to select an appropriate survey sample.

If you have unlimited resources or a small population of interest, probability sampling may not be necessary. But, in most cases, drawing a probability sample will save you time, money and a lot of frustration. You can’t usually survey everyone, but you can always give everyone the chance to be surveyed; this is what probability sampling accomplishes.

Sample target markets anywhere across the globe using SurveyMonkey Audience. Select a plan that works best for your business.