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.

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:

All male adults in Chicago who have an MBAA selection of male adults in Chicago who have an MBA
All interns and junior level employees in a large corporationA selection of interns and junior level employees in the corporation
All FIFA World Cup football matches that African and European nations play inA selected list of matches that African and European nations play in

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 differences between population and sample?

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

This is the entire group your research targets. This is a subset or unit in the group of interest.
A population is usually large.A sample, by definition, is always smaller than the population.
It’s usually impractical to gather information from large populations.The smaller size of samples makes it more practical to collect and analyze data.
Researchers collect data from a population by conducting a census.Researchers can use a simple survey to collect data from a sample.
Population studies or censuses are usually expensive.Sampling is cost-efficient.

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.

For more details on what is systematic sampling, check out our guide

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.

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

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