Population vs Sample in Market Research - A Comprehensive Guide

Introduction

Imagine that the National Forest Service wants to know how many trees in a forest are under attack from an insect called the chestnut borer. One way to do this would be to have someone climb and inspect every tree in the forest and count the number with chestnut borers. 

There are a lot of trees in the forest, though, so climbing and assessing every one of them could take years and millions of dollars. 

The Forest Service doesn’t have the labor, the time, or the money for that. Instead, they hire a statistician to take a sample of the trees in the forest and then infer how many trees in the forest suffer from chestnut borers. 

For example, after sampling 100 trees they find that 5 of them had chestnut borers. The statistician concludes that between 2.8% and 7.2% of the trees in the forest have chestnut borers (don’t worry about the numbers, we’ll get to that later).

Let’s use these two definitions: 

• “Population” refers to the totality of any entity of interest. 
• “Sample” refers to a subset of the population which we choose to investigate. 

What is Population in Market Research?

In research, “population” refers to the totality of whatever entity we’re trying to study – the entire group, including all its members. 

When we do primary research, it is the population that we’re trying to learn information about. In the forest example above, the population consists of all the trees in the forest. Marketing researchers also study populations. For a marketing researcher a population might be 

• People who plan to buy a car next year
• Customers who stayed at a Ramada Inn 
• Cold sufferers
• Etc.

In each case, it is the population that the researcher cares to learn about – we might sample 100 folks who stayed recently at a Ramada Inn, but we do so in order to learn, for example how satisfied the entire population of people who recently stayed at Ramada Inn were with their stays. 

Population Parameter versus Sample Statistic

A population parameter refers to a numeric value that characterizes an entire group of interest—the population. 

For instance, if we were to survey every customer of a national brand, any measure we calculate (like average monthly spending) represents a population parameter. 

In contrast, a sample statistic arises from a subset of the population—a sample. This statistic serves as a practical estimate of the corresponding parameter, but it is inherently an approximation, subject to sampling error.

Consider the customer satisfaction example above: If researchers aim to gauge the satisfaction of everyone who stayed at Ramada Inn recently, they would face the monumental task of collecting data from many thousands of people. 

Instead, they select a sample and might discover that the average political attitude score is 3.2 on a five-point scale. This 3.2 is a sample statistic, a proxy of the true mean—the population parameter—which represents the average satisfaction of all stayers. 

The use of samples is crucial in market research, as it underpins the strategies for estimation and hypothesis testing that help researchers extrapolate findings from a manageable number of survey responses to the much larger group of interest – the population.

Ways of Collecting Data from a Population

As mentioned, we could do a census – that is, measure every single member of the population – all the trees in the forest or every single person who stayed at Ramada Inn. Doing a census is extremely difficult and expensive (so expensive that only governments can afford to do them, and even then pretty infrequently). 

Instead, researchers doing primary research collect information only from subsets of the population and these subsets are called samples. It matters how researchers choose these subsets. 

More systematic and scientific ways allow the researchers to draw conclusions about the populations, but less rigorous ways do not. 

What Is a Sample in Market Research?

In marketing research, a sample is the subset of the population that we choose to study in order to learn about the population as a whole. 

What sampling technique we use is extremely important. 

A sample drawn scientifically can represent the population (it can allow us to make inferences, or conclusions about the population). 

A sample drawn carelessly likely will not represent the population and it may lead to incorrect conclusions about the population. 

The inaccuracy caused by carelessly drawn samples is called sample bias.

Sampling Method Types in Research

For your findings to be credible, you must thoughtfully determine a sampling method type that ensures your sample accurately reflects the larger group. This involves choosing a representative sampling strategy. Broadly, there are two main categories of sampling methods that can be utilized in your research:

• Probability sampling
• Non-probability sampling

Probability Sampling Method

Scientific samples that allow us to make inferences about the population are probability samples.

In a probability sample, each member of the population has a known, non-zero chance of being in the sample. That’s it: if these conditions are met - known, non-zero chance – then the sample is a probability sample and it can be used to draw accurate conclusions about the population. 

Common examples of probability sampling method types are: 

• Simple random sampling
• Systematic sampling
• Stratified sampling
• Cluster sampling

Separate documents describe these methods in detail.

Non-probability Sampling Method

On the other hand, non-probability samples happen when some population members have a zero or an unknown chance of being in the sample. 

Some common examples of non-probability sampling method types include:

• Convenience sampling
• Snowball sampling
• Judgment sampling

Again, a separate document will describe these methods in detail. 

Remember, it’s important to choose your sampling method type carefully. Only probability samples allow a researcher to draw conclusions about the population as a whole. 

Moreover, the different probability sampling methods have various strengths and weaknesses that favor one method in one situation and another in a different situation. 

How to Choose High-Quality Samples

A high-quality sample is a probability sample. There is a misconception that sample quality is related to sample size, but this is false. 

A smaller probability sample can be much more accurate than a much larger non-probability sample – because the probability sample allows the researcher to make inferences about the population while the non-probability sample does not. 

For a given type of probability sample, however, a larger sample size will be more valuable, because it will allow more precise inferences about the population as a whole (for example, our estimate of how many Ramada Inn guests were satisfied may have a margin of error of +/- 10% with a smaller sample but +/-5% with a larger sample. 

The math behind sample size is the subject of another document.  

Population vs Sample – Key Differences and Similarities

A population is the totality of the group you want to know about and a sample is a subset of that group that you use to learn about the population. 

Probability samples use probability theory to ensure that they can be used to draw conclusions about the population. Non-probability samples do not, and they cannot be used to draw conclusions about the population as a whole. 

Population vs Sample Examples

Example	Population	Sample Subset
Political Voters	All eligible voters in the country.	Randomly selected 1,000 registered voters from a state.
Smartphone Users	All smartphone users in the country.	Survey responses from 500 smartphone users in a city.
Product Defects	All products manufactured by a company.	Quality control inspection data from a batch of 100 products.
Website Visitors	All visitors to a website in a month.	Data from a random sample of 5,000 website visitors in a week.
Student GPAs	GPAs of all students in a university.	GPAs of 300 randomly selected freshman students.
Employee Satisfaction	Employee satisfaction levels in a corporation.	Survey responses from 150 employees across different departments.
Product Reviews	All online reviews of a specific product.	Analyzing 50 recent reviews of the product from a popular e-commerce platform.

Conclusion

Remember, we use probability samples to make inferences about the populations from which they come. Doing this allows the researcher to learn about their populations in a cost-effective manner. Using non-probability samples prevents the researcher from making valid inferences about the population.