Population vs. Sample - Definitions, Differences & Examples

When conducting research, a critical decision is whether to collect data from an entire population or just a sample. Understanding the distinction between these approaches is crucial for drawing valid and reliable conclusions.

A population is the complete set of individuals, objects, or units you’re interested in studying. This could be anything from people to animals, organizations, or events. The population represents the whole group about which you want to make inferences.

A sample is a subset of the larger population. When it’s not feasible or practical to collect data from an entire population, you can select a sample that adequately reflects the characteristics of the whole. The sample size plays a crucial role in ensuring that the findings from the sample are reliable, allowing researchers to make accurate inferences about the population.

In some cases, a cluster sample may be used, where the population is divided into groups, and only certain clusters are selected for the study, making data collection more manageable while still maintaining a representative sample.

Population vs sample

Population	Sample
All smartphones produced in 2023	100 randomly selected smartphones produced in 2023
Rivers in North America	20 major rivers in North America analyzed for water quality
All historical buildings in Europe	50 historical buildings in Europe surveyed for restoration needs
High school students in California	200 high school students from five schools in California
Articles published in scientific journals	100 articles published in the top 10 scientific journals in 2022

Collecting Data from a Population

When your research question requires data from every member of the group you’re studying or when you have access to the complete set of individuals, collecting data from the entire population can be the best approach.

Populations are most feasible when the group is small, accessible, and willing to participate. In these cases, getting data from every member of the population can provide a complete, unbiased representation of the characteristics you’re interested in.

Example

The HR manager of a small company with 30 employees wants to assess job satisfaction levels. Since the population is small and accessible, the manager collects data from all 30 employees through an anonymous survey. The manager can obtain a complete and unbiased representation of the company’s overall job satisfaction without sampling by gathering information from the entire population.

While collecting data from an entire population is ideal, sampling is often a necessary and valuable approach, particularly for large or dispersed groups. Choosing these two methods depends on your research project’s specific goals and constraints.

Collecting Data from a Sample

When your population of interest is large, widely dispersed, or difficult to access, collecting data from a sample rather than the full population becomes necessary. Using statistical analysis techniques, you can estimate or test hypotheses about the broader population based on the sample data.

Example

A market research firm is hired to evaluate customer satisfaction with a national retail chain. With millions of customers nationwide, collecting data from the entire population is impractical. Instead, the researchers use a stratified random sampling method to select a representative sample of 1,000 customers based on factors such as age, gender, and location. By analyzing the sample data and applying appropriate statistical techniques, the researchers can make inferences about the overall customer satisfaction levels in the national population without the need to survey every customer.

Probability sampling methods, such as simple random sampling or stratified sampling, are ideal for selecting a representative sample and minimizing bias. However, practical constraints often lead researchers to use non-probability sampling methods based on specific criteria, convenience, or other non-random factors.

While non-probability samples can be more cost-effective and practical, they may not fully represent the population, resulting in weaker statistical inferences and limited generalizability compared to probability samples.

There are several vital reasons why sampling is often necessary and beneficial for researchers:

Necessity: Studying the entire population may not be feasible due to its large size or inaccessibility.
Practicality: Collecting data from a sample is generally easier and more efficient than reaching the total population.
Cost-effectiveness: Sampling reduces the participant, laboratory, equipment, and researcher costs involved.
Manageability: Working with smaller sample datasets is more feasible and reliable for data storage and analysis.

Population Parameter versus Sample Statistic

When working with populations and samples, it’s important to distinguish between population parameters and sample statistics:

Population parameters are the true, underlying values that characterize the entire population, such as the mean, median, or standard deviation.
Sample statistics are the calculated values based on the data collected from a sample, which are used to estimate the corresponding population parameters.

Example

If you measure the heights of all 10,000 students at your university, the average height you calculate is a population parameter. However, if you measure the heights of only 500 randomly selected students, the average height you calculate is a sample statistic that estimates the proper population parameter.

Samples allow you to make inferences about the larger population, but sample statistics will always have some degree of variability and potential bias compared to the true population parameters.

Sampling error

Sampling error is the difference between a sample statistic and the corresponding population parameter. Uncertainty arises when using a sample to estimate or make inferences about the characteristics of a larger population. Sampling error occurs because a sample is only a subset of the population, and the characteristics of the sample may not perfectly represent those of the entire population.

Note: A sampling error is not a mistake or a flaw in the research design. Instead, it is an unavoidable consequence of using a sample to conclude a population. Even when a sample is randomly selected and representative of the population, there will still be some sampling error due to chance variations between the sample and the population.

Practice Questions: Populations vs. Samples

Here are some practice questions to guide you on populations and samples:

1. A researcher wants to study the average height of students in a university with 20,000 enrolled students. The researcher selects a random sample of 500 students and measures their heights. What is the population in this scenario?

A. The 500 students in the sample

B. All students in the university

C. All university students in the country

D. The researcher

2. A political pollster surveys to estimate the proportion of voters who support a particular candidate in an upcoming election. The pollster randomly selects 1,200 registered voters from a total of 500,000 registered voters in the district. What is the sample in this context?

A. The 500,000 registered voters in the district

B. The 1,200 randomly selected registered voters

C. All voters in the country

D. The political pollster

3. A quality control inspector must determine the proportion of defective products in a large shipment of 10,000 units. The inspector randomly selects 200 units and examines them for defects. What is the sample in this scenario?

A. The 10,000 units in the shipment

B. The 200 randomly selected units

C. All products manufactured by the company

D. The quality control inspector

4. A psychologist is interested in studying the average stress levels of working adults in a city with a population of 500,000. The psychologist recruits 300 working adults through advertisements and measures their stress levels using a standardized questionnaire. What is the population in this scenario?

A. The 300 working adults in the sample

B. All working adults in the city

C. All adults in the city

D. The psychologist

5. A market researcher wants to estimate the average monthly grocery spending for households in a country with 50 million households. The researcher obtains a list of all households and uses systematic sampling to select every 5,000th household, resulting in a sample of 10,000 households. What is the population in this scenario?

A. The 10,000 households in the sample

B. All households in the country

C. All households in the world

D. The market researcher

6. A biologist wants to estimate the average weight of a specific fish species in a lake. The biologist catches 100 fish from various locations in the lake and measures their weights. What is the sample in this scenario?

A. All fish in the lake

B. The 100 fish caught by the biologist

C. All fish of the specific species in the world

D. The biologist

7. A school administrator wants to determine the proportion of students participating in extracurricular activities. The administrator surveys all 500 students in the school. What is the population in this scenario?

A. The 500 students in the school

B. All students in the district

C. All students in the country

D. The school administrator

8. A researcher wants to estimate the average income of a town’s residents. The researcher randomly selects 100 residents and asks them about their income. What is the sample in this scenario?

A. All residents of the town

B. The 100 randomly selected residents

C. All residents in the country

D. The researcher

9. A company wants to assess employee satisfaction. The human resources department surveys 200 randomly selected employees out of the company’s 1,000 total employees. What is the population in this scenario?

A. The 200 employees surveyed

B. All 1,000 employees in the company

C. All employees in the industry

D. The human resources department

10. A medical researcher wants to determine the effectiveness of a new drug in treating a specific condition. The researcher conducts a clinical trial with 500 patients randomly assigned to receive either the new drug or a placebo. What is the sample in this scenario?

A. All patients with the specific condition

B. The 500 patients in the clinical trial

C. All patients in the hospital

D. The medical researcher

The answers: