Population vs sample in research: What’s the difference?

Population and sample are two important terms in research. Having a thorough understanding of these terms is important if you want to conduct effective research — and that’s especially true for new researchers. If you need a primer on population vs sample, this article covers everything you need to know, including how to collect data from either group.

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What is a population?

Outside the research field, population refers to the number of people living in a place at a particular time. In research, however, a population is a well-defined group of people or items that share the same characteristics. It’s the group that a researcher is interested in studying.

Arvind Sharma, an assistant professor at Boston College, explains that a population isn’t limited to people: “It can be any unit from which you obtain data to carry out your research.” This group could consist of humans, animals, or objects.

Below are some examples of population:

• Male adults in the United States
• World Cup football matches
• Insects in American rainforests

As you can see from the examples above, populations are usually large, so it’s often difficult to survey an entire population. That’s where sampling comes in.

What is a sample?

A sample is a select group of individuals from the research population. A sample is only a subset or a subgroup of the population and, by definition, is always smaller than the population. However, well-selected samples accurately represent the entire population.

Below are some examples to illustrate the differences between population vs sample:

The sample a researcher choses from any population will depend on their research goals and objectives. For example, if you’re researching employees in a large corporation, you may be interested in C-level executives, junior-level employees, or even external contractors.

What are the similarities between a population and a sample?

Although the population and the sample differ in size and scope, they share several key elements that are crucial for accurate research. Understanding these similarities helps clarify why data collected from a sample can represent an entire population.

1. Definition by research objectives
   Both the population and the sample are established based on specific research goals. The definition of “who or what is being studied” always comes first. Researchers identify shared characteristics, such as age, profession, location, or behaviors, to define both groups.
   
2. Units of analysis
   In both cases, the basic unit of data collection (individuals, households, companies, objects, etc.) remains consistent. For example, if your research unit consists of individual students, then both the population and sample will be made up of students.
   
3. Methods of data collection
   Whether collecting data from a full population or a sample, researchers gather the same types of data using similar tools, including surveys, interviews, observations, experiments, and secondary data.
   
4. Application of statistical techniques
   Many statistical tools — such as mean, median, standard deviation, or regression — are used to analyze both populations and samples. In the case of samples, these results are used to make assumptions about the population.
   
5. Potential bias and error
   If not designed or executed properly, both population studies (e.g., censuses) and sample-based research can suffer from nonresponse bias, measurement error, or data processing issues.

What are the differences between population and sample?

Below are the main differences between a population and a sample, as pointed out by Sharma:

Key steps in the sampling process

Choosing a sample isn’t just about selecting a few individuals at random. It’s a structured process that ensures the data collected is representative and reliable. Here are the essential steps involved in drawing a research sample:

1. Define the target population
   Start by identifying the full group of interest. Clearly describe the characteristics that define this group (e.g., all female university students in Canada aged 18–25).
   
2. Develop a sampling frame.
   This is a list or source that contains all aspects of the population the sample will be drawn from. For example, a school’s student database can serve as a sampling frame.
   
3. Select the sampling technique.
   Choose the method that best fits your research design:
   
   • Probability sampling (e.g., random, systematic, stratified) ensures each member has a known chance of being selected.
     
   • Nonprobability sampling (e.g., convenience, snowball) is often used when a complete list is unavailable or hard to access.
     
4. Determine the sample size.
   Use statistical formulas or online calculators to decide how many individuals you need in your sample to achieve reliable results, based on the size of the population, confidence level, and margin of error.
   
5. Select the sample.
   Implement your chosen technique to pick your sample from the sampling frame. This is where randomness, stratification, or other methods come into play.
   
6. Collect data from the sample.
   Administer your survey or experiment, checking for consistency, accuracy, and ethical considerations (e.g., informed consent).
   
7. Assess representativeness.
   After data collection, compare the sample’s demographic or behavioral characteristics to those of the full population (if known) to confirm that it’s representative of the population.

What are some reasons for sampling?

Collecting data from an entire population isn’t always possible. “In fact,” explains Sharma, “99 percent of the time, we can’t survey the entire population. Other times, it is not even necessary.

“A representative sample drawn using appropriate sampling techniques will provide results that are representative of the entire population. So, it would be unnecessary to survey every member of the population.”

Below are the other most important reasons for using sampling.

1. Cost

Population studies are more expensive than sample surveys. For example, researching the entire population of adult male Americans would be too costly. It’s more cost-effective to work with a representative sample.

2. Practicality

Consider the adult male American research example. Even if a researcher had the resources to survey all the males in that population, it may be difficult or impossible to obtain responses from all participants. For example, the researchers may not even be able to contact all members of this population.

3. Manageability

It’s easier to manage time, costs, and resources when working with samples. Also, it’s easier to manage the data you collect from a sample vs a population. For example, it’s easier to analyze data from a sample of 1,000 adult males than a sample of all adult males in the U.S. or even a specific state.

How can you collect data from a population?

Collecting data from an entire population requires a census. A census is a collection of information from all sections of the population. It’s a complete enumeration of the population, and it requires considerable resources, which is why researchers often work with a sample.

If the target population is small, however, then you can collect data from every member of the population. For example, you can survey the performance of the members of the customer service team in a bank branch. The number is likely to be more manageable, so you can access and collect data from this population.

What methods can you use to collect data from a sample?

There are so many approaches for collecting data from samples. Some of the more commonly used methods are listed below.

1. Simple random sampling

In simple random sampling, researchers select individuals at random from the population. In this method, every member of the population has an equal chance of being selected.

For example, suppose you want to select a sample of 50 employees from a population of 500 employees. You could write down all the names of the employees, place them in a hat or container, and pick employee names at random like you would in a lottery. That’s an example of simple random sampling. It works best when the population isn’t too large.

2. Systematic sampling

This is a sampling technique that selects every kth item from the population. It’s a type of probability sampling researchers use to select items from a population randomly. A researcher may want to use this technique if they’re working with a large population and need to sample only a small number of items in order to study them in detail.

For example, to apply systematic sampling in a performance survey of 1,000 customer service team members, we can choose every fifth member — i.e., the fifth, 10th, 15th customer service rep, and so on.

3. Stratified sampling

In this probability sampling method, researchers divide members of the population into groups based on age, race, ethnicity, or sex. Researchers select individuals randomly from those groups to form a sample. This ensures that every group is equally represented.

What is a sampling error?

A sampling error is the difference between the value obtained from a sample and the true population value. It’s the difference between an estimate from a sample and the true population value.

A sampling error can occur if you don’t have enough people in your sample or if you select people who aren’t representative of the population. This can impact the accuracy of your survey. For example, if you want to know what percentage of adults are vegetarian but only ask vegetarians in a specific city, then this would be an example of selecting people who aren’t representative of the population.

According to Sharma, you can reduce sampling errors by increasing the sample size. He also notes that sample design and variation within a population affect sampling errors.

Statistical inference using population and sample data

Statistical inference is the process of using information gathered from a sample to make conclusions about the entire population. This approach is common across multiple fields. Here are a few real-world examples of what this might look like:

Medical research

• Scenario: A pharmaceutical company wants to know if a new drug lowers blood pressure.
• Population: All adults with high blood pressure.
• Sample: 2,000 patients with high blood pressure from various clinics enrolled in a clinical trial.
• Inference: If the sample shows a significant drop in blood pressure, the company infers that the drug is effective for the broader population.

Market research

• Scenario: A company wants to understand customer satisfaction with a new product.
• Population: All customers who purchased the product in the last six months.
• Sample: A random selection of 500 customers who purchased the product in the last six months was surveyed via email.
• Inference: High satisfaction in the sample suggests similar satisfaction among the entire customer base.

Political polls

• Scenario: A polling firm wants to predict the outcome of a national election.
• Population: All eligible voters.
• Sample: 1,200 voters selected using stratified sampling by region, age, and political affiliation.
• Inference: Based on responses, the firm predicts which candidate is likely to win.

Educational studies

• Scenario: Researchers want to evaluate the effectiveness of a new math curriculum.
• Population: All middle school students in a country.
• Sample: 100 randomly selected schools from urban, rural, and suburban areas.
• Inference: If test scores improve significantly in the sample, the curriculum may be recommended for broader implementation.

Public health monitoring

• Scenario: A government health agency is tracking the spread of the flu.
• Population: All households in the country.
• Sample: A monthly rotating panel of 5,000 households.
• Inference: Infection rates in the sample are used to estimate national flu prevalence and guide policy decisions.

How can Jotform make the research process easier?

Whether you’re surveying a small or large sample or even an entire population, Jotform gives you the right tools to make your research easier. With Jotform’s free online survey maker, you can create engaging surveys and collect responses online. You can easily customize any of our 10,000-plus free survey templates to suit your research purposes. Get started with Jotform today.

Photo by Stanley Dai on Unsplash