8 Types of Survey Sampling + Pros & Cons, Errors & Bias, and More

Survey sampling is the process of selecting a smaller group from a larger population to conclude efficiently and accurately. This article explores eight key sampling methods—four probability sampling techniques (simple random, systematic, cluster, and stratified) and four non-probability methods (convenience, quota, purposive, and snowball)—explaining how each works, their pros and cons, and when to use them. It also discusses sampling errors and bias, such as selection and researcher bias, and how to minimize them through randomization and representative sampling. With clear guidance on determining sample size, choosing the right method, and ensuring representativeness, this guide helps researchers collect reliable, valid survey data for sound decision-making.

Today, there are about 125 million households in the United States. To help communities and the government understand the direction the country is moving in (and much more), the Constitution requires a census. This census is designed to count every resident of the country every 10 years. In smaller populations, such as a small school, it might be possible to survey all the students, but for large populations like the U.S., surveying everyone is not practical. So how does the U.S. Census Bureau go about surveying 125 million homes? It doesn’t. The office uses sampling to get the job done. In this blog, we’ll take a look at eight different types of sampling methods and much more.

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What is Survey Sampling?

Sampling is a method of surveying a small portion of the population that represents a larger whole. This approach is often referred to as a sample survey, which is a practical way to collect data from large populations without surveying every individual. To go back to our initial example, the U.S. Census Bureau surveys a sample population of 3.5 million households to arrive at conclusions about the population, such as employment, income, poverty, health, insurance coverage, educational attainment, and crime victimization.

Statistical surveys rely on sampling methods to efficiently gather data and draw conclusions about broader populations. While still an extremely large sample, it is significantly less than the 125 million households that make up the country (a surveying feat that would be near impossible). Thankfully, most surveys aren’t quite such an undertaking, and sampling makes them fairly easy to conduct.

History of Survey Sampling

So, who do we have to thank for the idea of sampling? Valery Ivanovich Glivenko and Francesco Paolo Cantelli. In the early 20th century, these two mathematicians discovered that observations randomly drawn from a population gradually take on the shape of the greater population as the sample size is increased. When a sufficient sample size is achieved, the sample will have characteristics that mirror those of the population, give or take a few points (called a margin of error). The difference between the sample and the population is known as sampling error, and minimizing sampling error is a key goal in survey design.

Determining Survey Sample Size

There are a number of considerations at play when determining sample size, or the number of people chosen for a survey from the total population of that demographic. To begin, most statisticians agree that the minimum sample size to get any kind of meaningful result is 100. If your population is less than 100, then you really need to survey all of them. In small populations, all population members can be surveyed directly, ensuring complete coverage.

So, let’s say you want to know what high school students think about virtual learning. Now, it’d be nearly impossible to survey every high school kid in the country, so you choose a sample to represent them. This could be a couple of thousand kids, for example, versus the millions of them out there. The final sample is the group of students actually selected after applying your chosen sampling technique, ensuring your results are as representative as possible. On the other hand, pretend you’re a small business owner with 40 employees. In that case, you’ll want to survey them all to arrive at meaningful conclusions.

Learn more about determining sample size and find a free margin of error calculator in our blog, margin of error.

Avoiding Survey Sampling Errors

Sampling isn’t always perfect, of course. Sampling errors can be introduced that lead to unacceptable margins of error. There are seven main types of sampling errors:

1. Sample Frame Errors. A sampling frame is the list or method used to identify the subset of the population to be sampled. Sample frame errors occur when the sampling frame does not include the entire target population, for example, only selecting people from a phonebook, which excludes anyone who is unlisted or doesn’t have a landline.
2. Selection Errors. This happens when the sample is composed of volunteers who feel strongly about the topic. However, by leaving out those who don’t feel as strongly, the results will likely be biased.
3. Non-response Errors. When not enough people respond to a survey, the sample size becomes smaller and thus is less likely to be representative of the larger population.
4. Population-specific Errors. This is common when a researcher is unsure of their target audience. For example, consider a survey about health issues among the elderly. Who should be targeted – the elderly, their caregivers, or their physicians?
5. Researcher Bias. This occurs when samples are deliberately chosen by a researcher rather than being selected randomly. Deliberate sampling could result in a researcher intentionally or unintentionally introducing their personal bias. This is most common in in-person surveys, when a researcher could choose participants based on gender, age, race, ethnicity, and so on.

For more, check out our blog 7 Types of Sampling Errors and How to Avoid Them or learn about the 5 types of leading questions with examples.

What is Sampling Bias?

Sampling bias occurs when researchers fail to choose participants properly or at random. When randomization isn’t used, a survey’s validity can be seriously impacted because it doesn’t accurately reflect the greater population. Using random selection of participants helps ensure each individual has an equal chance of being chosen, which reduces bias and increases the representativeness of the sample. It is important to randomly select participants to avoid bias and improve the reliability of your survey results.

A perfect example of sampling bias is demonstrated by an event back in 1948. At the time, Harry Truman and Thomas Dewey were battling it out for the United States Presidency. To predict the winner, a nationwide telephone survey was conducted, and the results heavily favored Dewey. The Chicago Daily Tribune, confident in the survey and wanting to be the first to break the news, printed its newspaper with the headline “Dewey Defeats Truman.” Of course, Dewey didn’t win, and the paper had egg on its face (the snafu resulted in this famous photo of ultimate winner Truman holding up the paper with the incorrect headline while smiling triumphantly).

What went wrong? In 1948, only wealthy families owned telephones, and they tended to favor Dewey. Those who couldn’t afford a phone mostly backed Truman, but they were completely left out of the survey due to the phone sampling bias. So, be sure to be aware of survey biases when sampling, and read more about it in our blog, Different Types of Survey Bias and How to Avoid Them.

Using proper sampling methods, such as random selection, is essential to draw valid conclusions from your survey data. Additionally, it’s important to consider ethical considerations in survey research to ensure integrity and respect for participants.

Main Types of Survey Sampling

There are two main types of sampling methods: Probability sampling and non-probability sampling. A probability sample is a group selected using probability sampling methods, ensuring each member of the population has a known chance of being chosen. In contrast, nonprobability samples are groups selected without randomization, using nonprobability sampling methods, which do not give every population member an equal chance of selection. Beneath each of these buckets fall four additional types of survey sampling. Let’s dive in.

Probability Sampling

With probability sampling, participants are selected completely at random with the goal of ensuring that everyone in the population has an opportunity to be surveyed. Thanks to randomization techniques (such as online random number generators, Excel’s RAND function, or simply drawing names from a hat), this method generally offers results that are representative of the target population. Probability sampling allows researchers to make more accurate inferences about the population because the process reduces bias and increases reliability. In this context, a sample statistic—such as the mean or proportion calculated from the sample data—is used to estimate the corresponding population parameter. Plus, by choosing participants at random, a researcher’s personal views and opinions cannot influence the sample. There are a few drawbacks:

• Ensuring randomization can mean more research must be done before crafting the survey.
• Data analysis following completion of the survey can be more complicated.
• If response rates are low, randomization minus those previously surveyed must be performed again.

Read more in our blog, What is Probability Sampling? Pros, Cons, and Examples.

Non-Probability Sampling

With non-probability sampling, randomization techniques are not employed, meaning that not everyone will have a fair chance of being surveyed. However, this makes it a fast and inexpensive way to collect data, as little research is required before surveying. Convenience samples, a type of non-probability sample, are quick and cost-effective because they involve selecting readily accessible individuals, but they may lack representativeness and introduce potential biases. A convenience sample is often used for preliminary or pilot studies due to its ease of access, though results may not be generalizable. Judgment sampling, also known as purposive or expert sampling, is another non-probability sampling method where researchers use their expertise and knowledge of the target population to deliberately select participants, making the process subjective. This makes it a great starting point in which to form quick hypotheses and then determine if further probability sampling would be beneficial. Another plus? If response rates are low, the researcher can simply continue sending surveys until they meet their goal without having to worry about randomization. Of course, there are drawbacks here as well:

• Because participants tend to be surveyed based on their ease of access rather than randomly, there’s a high chance they may not be representative of the greater population.
• Samples can sometimes be filled with people who want to be part of research because they want the incentive or hold strong views that they want to share, skewing results.
• There is a high potential for sampling bias because sample selection is deliberate. This means a researcher’s personal views and opinions could easily influence the sample, or he or she may be selective about who they choose to participate.

Read more in our blog, What is Non-Probability Sampling: Pros, Cons, and Examples.

Example of Probability and Non-Probability Sampling

To better understand these sampling methods, let’s consider a few examples of sampling. Let’s say you’re the owner of Bob’s Bait & Tackle. It’s a relatively small business, though you do have a database of 5,000 customers. You want to know how your survey participants feel about your store, but collecting data from all 5,000 of them would be too time-consuming. So, you survey 10% of them, or 500.

With probability sampling, all customers must have an equal chance of being sampled. So, you use a number generator (1–5,000) and choose 500 customers at random who correspond with the numbers and survey them. In this scenario, all 5,000 customers had the opportunity to be part of the survey, so there is less opportunity for survey bias or error. The goal of sampling is to conclude the larger customer base based on the responses from your sampled survey participants.

With non-probability sampling, you’re not going to worry about randomization; you just want to quickly and easily survey 500 people total. So, you simply survey the first 500 customers in your database. 4,500 people were effectively excluded, which increases the chances of survey bias or sampling errors, but allows you to complete the survey quickly and easily.

Probability Sampling Methods

There are four probability sampling techniques, which produce probability samples that are highly representative of the target population. The sampling process for these techniques involves carefully selecting participants to ensure each member of the population has a known chance of being included. These methods allow researchers to draw precise conclusions from their data due to the accuracy and reliability of the results. Here’s a quick look at each.

1. Simple Random Sampling

This is the easiest way of randomly sampling from a population. Using random selection, you assign numbers to everyone in the population and then randomly select those numbers (pull them from a hat if it’s a small sample, or use an automated process if it’s large). Researchers randomly select individuals to ensure each person has an equal chance of being chosen, which increases representativeness and reduces bias. The sample statistics calculated from the selected sample are then used to estimate the population parameter, which is the fixed value that characterizes the entire population. Read more: Simple random sampling.

2. Systematic Sampling

Systematic sampling is a straightforward method for selecting a sample, where researchers draw a random sample from the target population by choosing units at regular intervals starting from a random point. This is often referred to as “choosing every ‘nth’” (e.g., 5th, 10th, 12th, etc) individual. The process results in a final sample of individuals selected at these regular intervals. Read more: Systematic Sampling.

3. Cluster Sampling

This sampling technique divides the target population into groups or “clusters.” Then, the researcher selects sampled clusters, either choosing entire clusters or individuals within them, to participate in the survey. This method is especially useful for large populations that are geographically dispersed and share similarities (e.g., number of children, occupation, or college major). Read more: Single, Double, and Multistage Cluster Sampling.

4. Stratified Random Sampling

This method involves dividing a large population into smaller groups that typically don’t overlap but together form a representative sample of the entire population. Often, this means classifying groups by demographic factors such as gender, age, race, ethnicity, etc. To determine samples, the researcher splits subjects into mutually exclusive demographic groups and then uses simple random sampling to select members. This approach allows researchers to make more accurate inferences about each subgroup and enables them to draw precise conclusions from their data.

Non-Probability Sampling Methods

There are four nonprobability sampling methods, which are techniques that do not involve randomization and result in nonprobability samples. These samples may not be representative of the entire population, as not all members have an equal chance of being selected. Here’s a quick look at each.

1. Convenience Sampling

This sampling method is quick and easy, involving surveying a convenience sample—those individuals who are most readily accessible. In this type of sampling, the researcher doesn’t care whether the sample is representative of the entire population; everyone is eligible. For example, a college student wants to learn about alcohol consumption among undergraduates, so she surveys people in her dorm because they’re conveniently located. However, convenience samples have significant limitations: they often lack representativeness and can introduce potential bias, making it difficult to generalize results to the broader population. Read more: Convenience sampling.

2. Quota Sampling

Similar to convenience sampling, however, the researcher has a specific number of people they intend to survey (e.g., 50 men and 50 women). This provides a more diverse number of respondents from different subpopulations. Read more: Quota sampling.

3. Purposive Sampling

With this technique, known as purposive sampling or judgment sampling, the researcher uses their expertise and understanding of the survey’s purpose, along with their knowledge of the population, to decide who to include in the sample. This non-random sampling method relies on human judgment to select participants based on their relevance or expertise. They may choose one of the following purposive sampling methods:

• Heterogeneity sampling: collecting the widest range of opinions from different people.
• Homogeneous sampling: collecting opinions from like-minded participants.
• Deviant sampling: collecting opinions from people with a specific or unusual trait.
• Expert sampling: collecting opinions from experts on the topic.

4. Snowball Sampling

Researchers use snowball sampling when targeting specific, hard-to-reach groups. These people may be hesitant to participate, for example, illegal immigrants, drug users, or those with health conditions. In these instances, the researcher uses people who fit the necessary criteria to recruit others in their social circle who also fit. In this way, the sample size “snowballs,” becoming larger as more recruits participate, resulting in a more accurate survey.

Representative Sampling: What It Is and Why It Matters

Representative sampling is at the heart of reliable survey research. It refers to the process of selecting a sample that mirrors the characteristics of the entire target population, ensuring that the findings from your survey can be confidently generalized to everyone you want to study. Achieving representative sampling means that every member of the population has an equal chance of being selected, which is why probability sampling methods—like simple random sampling, stratified sampling, and cluster sampling—are so important.

By using these probability sampling techniques, researchers can create a sample that accurately reflects the diversity, demographics, and behaviors of the target population. For example, if your target population includes people of different ages, genders, or backgrounds, representative sampling ensures that your sample includes these groups in the same proportions as the whole population. This approach minimizes bias and increases the likelihood that your survey results will lead to valid conclusions about the entire population.

In practice, representative sampling allows you to draw meaningful insights from your data, knowing that your sample is a true microcosm of the larger group. Whether you’re conducting market research, academic studies, or organizational surveys, using the right type of sampling—especially probability sampling—helps you avoid skewed results and supports sound decision-making.

Target Audience and Survey Sampling

Defining your target audience is a foundational step in any successful survey research project. The target audience is the specific group of people you want to reach with your survey—whether it’s a broad population like all adults in a country, or a more specific group such as recent college graduates or customers of a particular product.

Once you’ve clearly identified your target audience, choosing the right sampling methods becomes essential. The sampling method you select—whether it’s probability sampling or non-probability sampling—will determine how well your sample represents the target audience. For instance, if your survey is aimed at a specific group, such as healthcare professionals, using targeted sampling techniques ensures that your sample accurately reflects the views and experiences of that group.

Survey sampling methods like stratified sampling or quota sampling can help you include the right mix of participants, while convenience sampling might be used for preliminary research or when time is limited. By aligning your sampling techniques with your target audience, you can collect data that is accurate, reliable, and truly representative of the people you want to understand.

How to Choose the Correct Sampling Method

Selecting the correct sampling method is a crucial part of the survey research process. The right sampling method ensures that your sample is appropriate for your research goals, fits the characteristics of your target population, and works within your practical constraints. Here’s how to approach this important decision:

Define Your Research Goals

Start by clarifying what you want to achieve with your survey. Are you aiming to gather opinions from a specific group, or do you need insights that apply to a broader population? If you want to learn more about the types of survey methods, a comprehensive guide can help you decide which approach best fits your research goals. If your research goals require precise, generalizable results, probability sampling methods like simple random sampling or stratified sampling are often the best choice. For example, if you want to understand the preferences of different age groups, stratified sampling can help you ensure each group is properly represented. On the other hand, if your goal is to quickly gather feedback from a hard-to-reach or specialized group, non-probability sampling methods such as snowball sampling or convenience sampling may be more practical.

Assess the Nature of Your Population

Consider the characteristics of your target population. Is it large and geographically dispersed, or small and relatively homogeneous? If your population is spread out or diverse, cluster sampling can be an efficient way to gather data from different segments. For more uniform populations, stratified sampling allows you to ensure that key subgroups are included in your sample. Understanding the makeup of your population will help you select a sampling method that captures the full range of perspectives you need.

Consider Your Constraints

Finally, take into account any limitations you may face, such as time, budget, or accessibility. If you’re working with limited resources, non-probability sampling methods like convenience sampling or quota sampling can help you collect data quickly and cost-effectively. However, if you have the time and resources to invest, probability sampling methods such as simple random sampling or systematic sampling can provide more accurate and reliable results. By balancing your research goals, population characteristics, and practical constraints, you can choose the sampling method that best fits your needs and ensures your sample is both representative and robust.

Conclusion

Online surveys are a great way to conduct both probability and non-probability sampling, and they are an effective tool for data collection from large populations. Online surveys allow you to cast a wide net, reaching across demographics and geographical boundaries, making it easier to conduct research across diverse groups. With SurveyLegend, our online surveys are easy to create and easy on the eyes – that’s because you can add pictures to surveys, boosting engagement, triggering respondent emotion and memory, and crossing language barriers. Below is an example of one of our photo surveys that plays off the example earlier of a college student surveying classmates about alcohol.

The survey below is LIVE, so give it a try! If you’re interested in learning more about different types of student surveys and seeing live examples, check out this guide.

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