What is Non-Probability Sampling? Pros, Cons, and Examples

Non-probability sampling is a non-random sampling method where not every member of a population has an equal chance of being selected. It’s commonly used in exploratory or qualitative research when time, cost, or accessibility limit random sampling. Key types include convenience sampling, quota sampling, purposive sampling, and snowball sampling. This approach is fast, flexible, and cost-effective, making it ideal for preliminary research and reaching niche groups. However, because selection depends on judgment or accessibility, it introduces a higher risk of bias and limits generalizability. Despite these drawbacks, non-probability sampling remains a practical method for gathering quick, targeted insights.

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In this blog, we look at how it works, different types of non-probability sampling, how it differs from random sampling, and sampling advantages and disadvantages. A probability sampling method, in contrast, uses a sampling frame—a complete list of the population—to ensure each member has a chance of being selected, resulting in a random sample. We’ll also discuss the pros and cons of each approach, including when to use a non-probability sampling method or a probability sampling technique.

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Non-Probability Sampling Definition

Non-probability sampling methods recognize that not everyone will have the chance to take a survey. This is the opposite of probability sampling, which aims to ensure that everyone in the population has an equal chance of receiving a survey.

To better understand the difference between non-probability sampling vs probability sampling, consider a store owner surveying his customers. He has a customer database of 5,000. Since he can’t survey them all, he decided to survey 10% of them. With probability sampling, which requires all customers to have an equal chance of participating, he uses a number generator (1–5,000) to select 500 customers at random that correspond with the numbers generated. This process creates a simple random sample, where each customer has an equal probability of being chosen. Therefore, anyone from the population could have been selected, reducing potential survey bias.

With non-probability sampling, he doesn’t care that everyone has an equal chance of being chosen; he simply wants to survey 500 customers. This approach is sample-based, meaning the selection is based on convenience or other specific criteria rather than randomization. So, he sends an online survey to the first 500 customers in his database. Or, he could survey each customer who comes into his store until he reaches 500. Either way, 4,500 people did not have the chance to receive a survey, which can increase the chances of survey bias. We’ll discuss the pros and cons of this shortly.

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Comparison to Probability Sampling

When deciding how to collect data for a research project, one of the most important choices is between probability sampling and non-probability sampling methods. The key distinction lies in how sample members are selected from the target population.

With probability sampling, every member of the population has an equal chance of being chosen. This is achieved through random selection techniques such as simple random sampling, stratified random sampling, and cluster sampling. These probability sampling methods are designed to create a representative sample, minimizing sampling bias and allowing researchers to generalize their findings to the entire population. For example, stratified sampling ensures that specific characteristics or subgroups are proportionally represented, while cluster sampling can efficiently gather data from larger, geographically dispersed populations.

Unlike probability sampling, non-probability sampling methods—such as convenience sampling, quota sampling, snowball sampling, and purposive sampling—do not guarantee that every individual in the population has an equal probability of selection. Instead, these non-probability sampling techniques often rely on subjective judgment, accessibility, or referrals, which can introduce bias and limit the ability to generalize results to the broader population. For instance, a convenience sample may only reflect the views of those who are easiest to reach, while a snowball sample might overrepresent particular groups within a social network.

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The choice between these sampling methods often depends on the research objectives and available resources. Probability sampling is ideal when the goal is statistical generalization and when a high degree of reliability and validity is required. This approach is commonly used in large-scale surveys and quantitative research where the aim is to make inferences about population parameters.

On the other hand, non-probability sampling is frequently used in exploratory research, qualitative research, or when targeting specific groups that are difficult to reach through random sampling. These methods are especially valuable for gathering initial insights, conducting targeted research, or when time and budget constraints make probability sampling impractical. For example, purposive sampling allows researchers to focus on participants with specific characteristics relevant to the research aims, while quota sampling ensures that certain subgroups are included, even if the sample is not randomly selected.

It’s also worth noting that researchers sometimes combine both approaches within a single study. For example, they might use quota sampling to ensure representation of a specific group, then apply stratified sampling to achieve a more representative sample of the broader population.

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In summary, probability sampling techniques offer the advantage of statistical generalization and reduced sampling bias, making them suitable for studies where representativeness is crucial. Non-probability sampling methods, while less generalizable, provide flexibility, speed, and cost-effectiveness, making them ideal for exploratory studies or research focused on particular groups. By understanding the strengths and limitations of each sampling method, researchers can select the approach that best aligns with their research objectives and ensures meaningful, actionable insights.

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Non-Probability Sampling Methods

As mentioned earlier, there are different ways to go about obtaining a non-probability sample, also known as a non-probability sampling technique. So, how many types of non-probability sampling are there? Generally, a researcher will select one of three non-probability sampling techniques. Common non-probability sampling examples include snowball sampling, where existing participants recruit future subjects, and purposive sampling, where participants are selected based on specific characteristics relevant to the research.

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1. Convenience Sampling

This is the quickest and easiest type of non-probability sampling. With convenience sampling, as the name implies, all that matters is convenience. Researchers rely on convenience sampling to conduct research quickly and efficiently when time or resources are limited. This means the results are typically going to be less than scientific and therefore not applicable to the wider population. For example, a college student wants to learn about alcohol consumption among undergraduates, so she surveys people in her dorm because they’re easily accessible to her (i.e., convenient).

However, students in dorms are probably less likely to drink compared to those living off campus due to dorm rules, their age, and so on, so it’s not a representative sample. Her dorm may also be all female, leaving out all male students.

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It’s important to note that there are two other subtypes of convenience sampling: consecutive sampling, in which results are analyzed following each survey and the surveying continues until a conclusion can be reached; and self-selection (also known as volunteer sampling), in which volunteers sign up to be part of the survey. For a probability-based alternative, you may consider systematic sampling, which uses a fixed interval approach to select participants. Read more about convenience sampling.

2. Quota Sampling

Quota sampling is similar to convenience sampling in that anyone convenient to the researcher can be surveyed. The one difference is that there are specific targets for the number of people that need to be surveyed (e.g., 50 men and 50 women). So, using the student in an all-female dorm example, she could survey 50 girls in her dorm, but would then need to go to a male dorm and survey 50 boys as well.

While this is still not the most scientific method, it at least gets a more diverse number of respondents from different subpopulations and can provide deep insights into each subgroup. Read more about quota sampling.

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However, it’s important to note that because quota sampling relies on non-random selection within each group, it can introduce selection bias.

3. Purposeful Sampling

With the purposeful or purposive sampling method, also known as judgmental sampling, the researcher uses their understanding of the survey’s purpose and their knowledge of the population to make a conscious decision on who should be included in the sample to serve the overarching goal. Then, the researcher selects the participants accordingly. He or she may opt to do this in several ways:

• Heterogeneity sampling aims to collect the widest range of opinions and perspectives on a given topic.

• Maximum variation sampling seeks to capture a broad spectrum of perspectives and understand the variability within the population.

• Homogeneous sampling, which aims to collect opinions from like-minded participants (they may all be the same age, gender, race, religion, and so on).

• Deviant sampling, in which participants are selected based on an unusual or special trait.

• Expert sampling, in which specialists on a particular topic are sought out to inform the survey or validate the results of a previous survey.

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4. Snowball Sampling

While this method is not common for a lot of surveys, snowball sampling is often put to use when a researcher needs to target specific groups that are hard to find or reach, or who may be hesitant to speak with them. Often, the topic is sensitive or personal, such as studies about illegal immigrants, drug users, or those with rare health conditions.

Therefore, researchers use a small pool of participants that they’ve found to “nominate,” through their social circle, others they know who fit the criteria. Often, incentives will be provided to the participants to entice them since they may not be forthcoming otherwise. Because topics are often sensitive, Simply Psychology states that researchers must take precautions to protect the privacy of potential subjects, keeping names anonymous and using online encryption techniques.

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A related approach is total enumerative sampling, a form of consecutive sampling where all available members who meet the inclusion criteria are included in the study until it is complete.

Advantages and Disadvantages of Non-Probability Sampling

Non-probability sampling has both pros and cons. One of the main advantages of non-probability sampling is its simplicity, cost-effectiveness, and convenience, making it a practical choice for many research scenarios. Here’s a rundown of both that researchers need to be aware of.

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5 Pros of Non-Probability Sampling

1. It’s a fast and inexpensive way to collect data. Non-probability sampling streamlines the data collection process, requiring little research before surveying, as the researcher simply seeks out those easily within reach. If the researcher conducts non-probability sampling through an online platform, it becomes even easier, as there are no geographical limits.

2. It’s a great starting point from which to form quick hypotheses. Then, the researcher can determine if further probability sampling would be beneficial.

3. Low response rates don’t factor in, as the researcher continues surveying until they’ve reached their desired sample size. In self-selection sampling, the insights gained from volunteers’ opinions and motivations can provide valuable understanding for research studies. Or, in the case of consecutive sampling, surveying continues until they have enough data to conclude.

4. It enables researchers to connect with underrepresented or niche groups. This is usually accomplished through deviant sampling.

5. It allows real-time opinions on current events and topics. Because these surveys can be conducted on a whim, opinions can be gathered quickly and efficiently.

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5 Cons of Non-Probability Sampling

1. High risk of non-representative samples. Participants receive surveys based on convenience or ease of access. This means there’s a high chance they may not represent the greater population, undermining the validity of the results.

2. Difficult or impossible to calculate margin of error. Margin of error reflects how accurately survey results represent the total population, but without a defined sampling frame, it becomes unmeasurable.

3. Samples may attract people who want to participate. This may happen because they want an incentive or have strong opinions to share. This is common with self-selection and snowball sampling methods.

4. Sample sizes may be unclear. Because there is no way to measure the boundaries of the relevant population, determining the appropriate sample size can be challenging.

5. High risk of sampling bias. Because sample selection is deliberate, a researcher’s personal views, comfort level, or unconscious preferences could influence who they select. For example, they may approach only familiar demographics, which can greatly skew results.

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Non-Probability Sampling with SurveyLegend Online Surveys

Online surveys are a great way to conduct non-probability sampling. Online surveys can also be integrated into a research study as part of a broader research design, allowing for flexible data collection tailored to the study’s objectives. As noted earlier, sure, you can stand on a street corner or within a store and survey those who pass by or stop in. Or, you can cast a wider net by sending out online surveys. Since the “who” is not as important as the sample size, you can send out surveys to anyone – and as many as you like until you reach the desired sample size. Suppose you do have some specifics, for example, needing a certain number of men or women, or a particular age group. In that case, you can qualify respondents with some eligibility questions based on demographics.

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With SurveyLegend, our online surveys are easy to create and visually appealing. You can add pictures to surveys, which boosts engagement, triggers memory, and crosses language barriers. Below is an example of one of our surveys with images. This has been designed to match the student drinking survey we highlighted at the start of this blog.

A few things you’ll note:

• The survey begins with a welcome page describing the goal of the survey and includes a survey image.

• The first demographic question includes a qualifier. This way, the researcher will know when he or she has collected enough of each type of sample.

• Because gender identity can be a sensitive topic – but an important one, particularly for today’s younger generations – multiple choices are offered along with a “prefer not to answer” option.

• If a participant selects that they don’t drink, the questionnaire uses survey logic to immediately take them to the thank-you page.

• If a participant selects that they do drink, the survey continues with more questions.

• Various types of survey questions are used to engage participants: multiple choice, sliding scale, thumb ratings, emojis, picture questions*, and an open-ended question.

Once again, this survey is live, so try it out now. You can also refresh and retake it as many times as you like, as well as view live results. If you plan to use or reference survey data, make sure you know how to cite a survey in different citation styles.

Conclusions

Non-probability sampling is a quick and easy way to collect data. While there are multiple types of non-probability sampling, they all have one thing in common: They are not random. So, despite the ease of conducting them, there is the potential for survey bias. It’s up to each researcher to weigh the pros and cons of non-probability sampling. Then, it’s time to determine whether it’s the right method for the study. 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 non-probability sampling when surveying? What do you feel are the biggest pros and cons of this method? Let us know in the comments!

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