What is Non-Probability Sampling?
Learn more about this sampling method with our guide
Non-probability sampling is a sampling method that uses non-random criteria, such as availability or location, to select the consumers you want to survey.
It’s one of the most common sampling approaches used in research. It’s often used when speed, budget, or access to participants makes random selection impractical. Rather than aiming for statistical representativeness, non-probability sampling focuses on gathering insights from specific groups of consumers.
There are several different types of non-probability sampling, each suited to different research goals. Methods such as convenience, quota, purposive, and snowball sampling all offer practical ways to recruit participants, but they also come with trade-offs that you should understand.
In this guide, we’ll explain how non-probability sampling works, when to use it, the different methods, and the advantages and disadvantages to consider when choosing a sampling approach.
What is non-probability sampling?
Non-probability sampling is a research method where a sample is selected based on subjective criteria, such as availability or convenience, rather than random selection.
This is what separates it from probability sampling, where every member of your population has a known, equal chance of inclusion. This isn’t the case with non-probability sampling. Not all members of a population have an equal chance of selection.
Non-probability sampling is used in qualitative research, exploratory studies, and situations where a full population list either doesn’t exist or isn’t needed. It’s also the go-to approach when time or budget constraints make probability sampling impractical.
When should you use non-probability sampling?
Non-probability sampling works best in specific situations. Here’s when it makes sense to use it.
- You’re in the early stages of a project: Exploratory and pilot studies often use non-probability sampling to test ideas, test concepts, or messaging before committing to a larger piece of market research.
- Your target group is hard to reach: Some populations, people experiencing homelessness or members of niche communities, don’t appear in any standard sampling frame. Methods like snowball sampling let you reach them through referrals rather than random selection.
- You need participants with specific traits: If your research requires consumers who share a particular characteristic, a health condition, a job role, or a lived experience, non-probability sampling lets you recruit for it deliberately.
- The full population is unknown: No complete list of website visitors, app users or event attendees exists. Non-probability sampling gives you a practical route to data without needing one.
- Time or budget is tight: Probability sampling takes more resources. Non-probability sampling is faster and cheaper, which makes it a sensible choice for smaller studies or projects with tight timelines.
What’s the difference between non-probability and probability sampling?
The main difference between non-probability sampling and probability sampling methods is how the sample is selected.
As the name suggests, a probability sample is selected at random, while a non-probability sample is chosen based on certain criteria.
In probability sampling, everyone in your target population has an equal, known chance of being chosen. Selection is random, which makes the results statistically representative. So, you can generalise findings to the wider population with confidence.
In non-probability sampling, participants are chosen based on specific criteria rather than chance. Not everyone has an equal shot at being included. That makes it less representative, but faster, cheaper, and better suited to market research that doesn’t need to speak for an entire population.
Probability sampling
- Uses random selection
- Gives every member of a population an equal chance of being chosen
- Requires a sampling frame
- Best suited to quantitative, representative market research
- Slower and more expensive to run
Non-probability sampling
- Does not use random selection
- Not every member of a population has an equal chance of being chosen
- Does not require a sampling frame
- Best suited to qualitative, exploratory, or targeted market research, though it can also be used for quantitative studies, such as quota sampling for demographic analysis, where full probability sampling isn’t practical
- Faster and more cost-effective
Neither method is better than the other; they serve different purposes. The right choice depends on your research goals, your budget, and how much you need your findings to reflect the broader population.
What are the types of non-probability sampling?
There are four main types of non-probability sampling: convenience, quota, purposive and snowball. Each suits different research goals, so it’s worth understanding what sets them apart.
Convenience sampling
Convenience sampling means selecting participants based on how easy they are to reach. You recruit whoever is most accessible. Whether that’s people nearby, volunteers who respond to a post, or existing contacts.
It’s the quickest and cheapest method. This makes it popular for pilot studies and early-stage research. The trade-off is a higher risk of bias, since your sample reflects who happened to be available rather than your target population.
A common example: standing outside a shopping centre and stopping passersby or posting a survey link in a relevant online community.
Convenience sampling has two subtypes:
- Consecutive sampling: You work through eligible participants one at a time, analysing results as you go before moving on to the next. It’s more thorough than standard convenience sampling.
- Self-selection (volunteer) sampling: Participants opt in themselves, usually in response to an open call. It’s easy to run, but responses tend to skew towards people with strong opinions on the topic.
Quota sampling
Quota sampling divides your target population into subgroups, called strata, and sets a target number of participants to recruit from each. You fill those quotas through non-random means, usually convenience sampling.
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It’s similar in structure to stratified sampling, which allows you to get a more representative sample than convenience sampling. The key difference between quota and stratified sampling is that selection within each group isn’t random. You keep recruiting until you meet your quota for each subgroup.
There are two variants:
- Proportional quota sampling: The size of each quota reflects its proportion in the real population. This requires you to know the population breakdown in advance.
- Non-proportional quota sampling: You set fixed quotas for each subgroup regardless of their actual proportion in the population. Useful when the population size is unknown.
A typical example: a market researcher interviewing equal numbers of under-40s and over-40s about a product, regardless of the actual age split in the wider population.
Purposive sampling
Purposive sampling, also called judgmental sampling, means handpicking participants because they have specific qualities relevant to your research. You use your own judgement to decide who belongs in the sample.
It’s common in qualitative research, where you’re less interested in broad representation and more interested in depth. The obvious risk is researcher bias: the sample reflects your assumptions about who matters.
There are several subtypes of purposive sampling:
- Maximum variation (heterogeneous) sampling: You recruit across the widest possible range of perspectives on your topic. The goal is richness, not consensus.
- Homogeneous sampling: The opposite of heterogeneous sampling. You recruit participants who share a specific trait, so you can go deep on that group’s experience. For example, only including workers who have been in the same role for ten or more years.
- Typical case sampling: You focus on participants who represent what’s “average” or “normal” for the population you’re studying. Useful for building a baseline understanding of a group or setting.
- Extreme (deviant) case sampling: You focus on outliers: exceptional successes, notable failures, or cases that stand out from the norm. Often used to develop best-practice guidance.
- Critical case sampling: You select a single case (or a small number) that is particularly revealing. If it holds true here, it’s likely to hold true elsewhere, and vice versa.
- Expert sampling: You recruit participants specifically because of their knowledge or expertise. Useful in technical or specialised research, or when you want to validate findings with people who know the field well.
Snowball sampling
Snowball sampling is used when your target population is hard to find or not publicly visible. You start with one or two participants, then ask them to refer others who fit your criteria. The sample grows from there, like a snowball rolling downhill.
It’s useful if you want to research marginalised or hidden communities, where there’s no accessible sampling frame, and trust is important. Researchers studying topics like homelessness, addiction, or specific health conditions often use this method.
The main limitation is that your sample is shaped by the networks of your initial participants, which can introduce bias and limit diversity.
How do you choose the right non-probability sampling method?
The best method depends on three things: what you’re trying to find out, who your target participants are, and what resources you have available.
Use this as a guide:
- Use convenience sampling when you need fast, low-cost data and representativeness isn’t a priority. For example, a quick pulse check or an early-stage pilot.
- Use quota sampling when you want your sample to reflect specific proportions within a population, but don’t have the time or budget for full probability sampling. It works well for market research where demographic balance matters.
- Use purposive sampling when you need participants with very specific characteristics, or when the goal is depth over breadth. It’s the go-to for qualitative and exploratory research.
- Use snowball sampling when your target population is hard to reach, and there’s no existing sampling frame. It’s especially useful when trust and referral are key to accessing participants.
- Use self-selection sampling when you want to hear from people with a direct stake or strong interest in the topic. But you’ll need to factor in the likelihood of response bias when you analyse results.
If you’re still unsure, ask yourself: Does my research need to generalise to a wider population? If yes, consider whether probability sampling is more appropriate. If no, or if that’s not workable, non-probability sampling is often a better fit.
What are the advantages and disadvantages of non-probability sampling?
As with any sampling method, non-probability sampling has advantages and disadvantages. We’ve listed some of the most common and important below:
Advantages
- Cost-effective: As you don’t need to select a sample at random or need as much preparation time, it’s much cheaper and easier to conduct research. That makes non-probability sampling cheaper and more accessible, especially for smaller studies or internal research.
- Faster to run: You can target participants who are easier to reach, rather than waiting for a random selection process. That speeds up both recruitment and data collection.
- You don’t need a sampling frame: You don’t need a complete list of every individual in a population, which can be hard to find for large or unmapped groups.
- Flexibility: You can adapt your criteria or approach as your research develops. That makes it well-suited to qualitative, in-depth work where the scope might evolve.
- You can target specific subgroups: Non-probability methods make it possible to study niche populations, whether that’s rare disease patients, marginalised communities, or people with a very specific professional background, that random sampling would struggle to capture.
Disadvantages
- Selection bias: As the sample isn’t selected at random, some groups will be over- or under-represented. People who are more visible, more available, or more willing to take part will feature disproportionately, which can skew your results.
- Self-selection bias: When participants opt in voluntarily, they often have stronger views on the topic than those who don’t. That can make your findings less balanced than they appear.
- Researcher bias: With methods like purposive sampling, the researcher decides who gets included. That introduces subjectivity along with the risk that the sample reflects the researcher’s assumptions rather than those of the population.
- Limited generalisability: Because participants are not selected at random, the researcher’s bias can influence the results. This could mean that your results aren’t representative of the wider population.
- Unclear sample size boundaries: Without a defined population to work from, it can be difficult to know when you have enough participants. This can lead to over- or undersampling and wasted resources.
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