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Probability sampling is a sampling strategy that improves survey results. Learn how this type of sampling can provide the reliable results you need.

man reviewing probability sampling on a desktop computer


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.

  1. Everyone in the sampling frame must have an equal chance of being surveyed.
  2. You must know the chance of each person being selected. For example, you might determine that in a population of 100 people, each person’s odds of receiving a survey is 1 in 100.
  3. Sampling must be random to ensure that the sample 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 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.

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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:

  • Simple random sampling gives all members of the population an equal chance of being selected. 
  • Selection is done randomly. For example, researchers may use tools like a random number generator to select participants from a population.
  • Simple random sampling is prone to bias. The smaller the sample size compared to the population, the less likely it is to get a random sample.

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:

  • Stratified sampling draws a sample from each group (or stratum) separately to ensure every subgroup is represented.
  • It’s common to stratify by characteristics like sex, age, income bracket, or ethnicity.
  • Strata must be specific and exclusive, meaning every individual in the population should only be assigned to one group.
  • After splitting a population into strata, randomly select individuals from each group in proportion to the total population. Then, combine those individuals into a sample.

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:

  • Each cluster should have similar characteristics to the population. Instead of selecting individuals from each cluster, randomly select entire clusters. 
  • Include every individual from each selected cluster in the final sample. If the clusters are too large, randomly select individuals from each cluster. 
  • Researchers often use pre-established and easily available groups as clusters. Groups are based on geographic boundaries, like cities or counties, but they can also be schools or office locations.

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:

  • Each member of the population is assigned a number and then selected at regular intervals to form a sample. To put it another way, every “nth” individual in the population joins the sample.
  • It’s important to ensure that there’s no hidden pattern in the sample frame that may affect random selection. If there’s a risk of data manipulation, the sample may over or underrepresent characteristics.

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:

  • Study goals: It’s important to align sample design with objectives.
  • Target population: Understanding the population size, diversity, and size helps ensure the sample reflects it appropriately.
  • Sampling frame: Accurate data starts with a reliable and comprehensive list or database of the population.
  • Sample size: The size of your sample should balance statistical power and practicality. You might use a sample size calculator.
  • Data collection: How you plan to collect data (e.g., surveys and interviews) could impact your sampling method.
  • Feasibility and resources: Consider the practicality of reaching and recruiting participants as well as research budget, time, and resource availability.

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.

  1. Quota sampling: Like stratified sampling, quota sampling divides the population into subgroups based on known characteristics, traits, or interests. A cleaning company researching its popularity might split its population by age and gender. Then, it may take a sample from each group to meet a predetermined quota.
  2. Snowball sampling: This type of sampling relies on people in your population to identify others to sample. Say you’re researching local use of mobility ramps. Your population of interest is people in your city who use wheelchairs. You don’t have a complete list of these people, so probability sampling isn’t an option. However, the few identified survey respondents can connect you with other local people who use wheelchairs.
  3. Convenience sampling: In this approach researchers pull together a sample from individuals who are available and willing to participate. This is a convenient way to get fast data. However, like using a focus group or customer interviews, your findings won’t necessarily be representative. Still, they can give you qualitative insights.
  4. Judgmental: Judgmental sampling is frequently used in qualitative research. It entails researchers choosing the sample they believe will be most relevant. The mobility ramp researchers, for example, would do a purposive sample by choosing employees with disabilities to explore their needs.

There are several benefits to using probability sampling. 

  • It is cost-effective to sample large audiences representing your target audience.
  • Probability sampling is advantageous for geographically dispersed populations.
  • It requires little technical expertise when utilizing an agile experience management platform.

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.

  • 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. 
  • Simple and random probability sampling can provide quick results. Nonetheless, the clusters and strata might not be as targeted toward your intended audience.

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