Population vs Sample 

Population and sample in statistics are fundamental concepts because a population includes all the people, things, or data points a researcher aims to understand, whereas a sample is a smaller, more manageable part of that population chosen to study. 

In this blog, we will explain the concepts of population and sample, along with their applications and population and sample examples. We will also cover the key difference between population and sample to help you make valid inferences and draw accurate conclusions.

Table of Contents:

• What is Population in Statistics?
• What is Sample in Statistics?
• Why Sampling is Important in Statistical Research?
• Difference Between Population and Sample
• Sample Statistic vs Population Parameter: Key Differences
• Benefits of Using Samples Instead of Populations
• Common Mistakes When Choosing a Sample
• Impact of Sample Size on Data Accuracy
• Real-Life Examples of Population and Sample

What is Population in Statistics?

A population in statistics is the total collection of people, objects, or data to be analysed, or about which you will reach conclusions. In rare cases, researchers collect data from an entire population, such as in a national census or small, easily accessible groups.

• Complete Group: A population is all elements that meet a set of criteria established by the study. If you are researching college students’ stress levels in the US, then your population is all college students in the US.

• Not limited to people: A population can comprise people, animals, products, events, or even observations – anything on which you are conducting a study.

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Types of Population in Statistics

Populations can be categorized into different types based on their characteristics and scope.

• Finite Population: Countable, such as all employees in a company.
• Infinite Population: Theoretically uncountable, like all possible outcomes of rolling a die.
• Real Population: Actually exists, like all the bikes produced in 2025.
• Hypothetical Population: Based on assumptions or potential outcomes, like all results of a fair coin toss.

What is Sample in Statistics?

A sample in statistics is a selection of a subset of a population that is chosen to conduct a study or analysis. Collecting data from all members of a population is not practical due to the time, expense, or availability restrictions, so researchers select a subset that represents the larger group. For example, if your population is all high school students in the United States, your sample might comprise a thousand students from different geographic regions. 

Sample Statistic: 

• Sample mean (x̄): The average value in a sample. 
• Sample proportion (p̂): The proportion of the sample that has a particular characteristic. 
• Sample standard deviation (s): Indicates how far apart observations are from each other; it helps to show the spread or variability of the sample.

Representativeness: 

To achieve reliable results, the sample must be representative of the population. This means the sample must mirror the variety and diversity of the whole sample.

Types of Sampling Methods with Examples

Here are some of the key types of sampling methods:

• Random sampling: Every person in the population has an equal chance of being chosen. 
• Stratified sampling: The population is split into groups, and samples are taken from each group.
• Systematic sampling: You take the samples in a set pattern (e.g., every 10th person).
• Convenience sampling: Chosen for their convenience, generally not acceptable for accuracy, as it often leads to a biased sample in statistics.

Why Sampling is Important in Statistical Research?

Sampling in statistics is the most fundamental component of any research study, especially when the population is too large to be analysed completely. Sampling is a smart, effective, and reasonable approach that researchers can take to make trustworthy statements, as opposed to collecting information from every person or unit.

It is a scientific requirement in most cases. It is a better and more cost-effective way to acquire information, make decisions, and draw inferences about a larger population. If done correctly, sampling in statistics allows researchers to balance the trade-offs between efficiency, accuracy, and feasibility. This makes it a vital component of any successful research approach or strategy for efficient population and sampling in statistics. A proper sampling process ensures the sample accurately represents the population, minimizing bias and errors.

Difference Between Population and Sample

Population is the entire group under study, while a sample is a smaller, representative part of that group.

The table given below shows the difference between population and sample in statistics, which will explain it more efficiently.

Sample Statistic vs Population Parameter: Key Differences

Benefits of Using Samples Instead of Populations

Here are some of the benefits of using samples instead of populations:

1. Saves time: A sample can be analyzed much quickly than collecting information from a total population.
2. Saves cost: Sampling in statistics can reduce the costs of research by cutting data collection and data processing costs.
3. Easier for data management: The size of datasets to manage, store, and analyze is far smaller to deploy.
4. Faster results: useful to provide conclusions in the quickest timeframe when considering a larger dataset.
5. Takes population off the table: Sampling is the only option when it is unrealistic to use the whole population.
6. Enables contextual study: With the resource saved, it allows a thorough study of the sample.
7. Useful in destructive testing: In quality control, when destructively testing an item, you can destroy a small portion instead of the entire population.

Common Mistakes When Choosing a Sample

1. Using sample sizes that are too small: This means it may not be proportionate to the entire population.
2. Being biased in selection: Using only those who are easy to access (for convenience) or who you are already familiar with can tinge the results.
3. Not using an element of randomness: When there is no variability in the sampling, we may miss some important aspects of that variability in the population, because it won’t be random.
4. Not including all aspects of variability: If you miss groups and types, or people, then the sample will lack balance.
5. Not specifying the population you want to generalize to: If you don’t have a clear target group, you could miss the mark with your sample.
6. Including duplicates: Repeating records results in a sample that isn’t very accurate.
7. Poor timing: Data collection (whenever it may be) within a defined period of time should also be the proper timing to ensure valid information is obtained. 
8. Using records where people voluntarily respond to be included: The majority of the time is biased only to those who are volunteering to respond.

Impact of Sample Size on Data Accuracy

The size of a sample directly affects the accuracy and reliability of results, as shown below:

• Larger samples usually give more accurate and reliable results.
• Small samples can lead to errors and may not reflect the whole population well.
• More data reduces random errors and makes patterns clearer.
• Too large a sample can be costly and unnecessary if a smaller one gives similar results.
• Right-sized samples balance accuracy, time, and cost for population and sample in statistical studies effectively.
• Inadequate sample size may lead to false conclusions or low confidence in findings.

Real-Life Examples of Population and Sample

To understand how the concepts of population and sample are applied in real-world scenarios, let’s look at some real-life population and sample examples.

Population Examples

Case 1: 

• Goal: A company wants to measure the average salary of all employees.
• Population: All employees of the company.
• Reason: The company includes all of its employees. 

Case 2:

• Goal: A government health department wants to estimate the average life expectancy in a country.
• Population: All citizens of the country.
• Reason: The study uses the entire population of the country.

Sample Examples

Case 1:

• Goal: A researcher wants to examine the eating habits of university students.
• Sample: 200 students chosen from various application departments.
• Reason: The researcher examines only a sample of university students as a whole, not all.

Case 2:

• Goal: A polling agency wants to make predictions about election results.
• Sample: 1,000 registered voters chosen randomly from the population of registered voters.
• Reason: It is a selected group with characteristics that represent the larger voting population.

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Conclusion

Understanding the difference between a population and a sample is essential in statistical analysis. A population refers to the entire group under study, whereas a sample represents a smaller, manageable subset used to draw conclusions. Since studying an entire population is often impractical, using a well-chosen sample offers a practical, cost-effective, and time-efficient way to draw meaningful insights. Overall, the validity of a statistical study depends on how well the sample represents the population, so proper sampling methods are crucial for reliable results.

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Population vs Sample – FAQs