# What does population and sample mean in statistics?

All Courses
AI & Machine Learning
Data Science & Business AnalyticsAI & Machine LearningProject ManagementCyber SecurityCloud ComputingDevOpsBusiness and LeadershipQuality ManagementSoftware DevelopmentAgile and ScrumIT Service and ArchitectureDigital MarketingBig DataCareer Fast-trackEnterpriseOther Segments
ArticlesEbooksVideo TutorialsLive WebinarsOn-demand WebinarsFree Practice Tests
HomeResourcesAI & Machine LearningPopulation vs Sample: Definitions, Differences and Examples

Article

Article

Ebook

Article

Article

Video Tutorial
Article

Article
Webinar

Article

## Population vs Sample: Definitions, Differences and Examples

By Ravikiran A SLast updated on Sep 14, 20214383

View More

In statistics, data plays an essential role in deciding the validity of the outcome. The data being used must be relevant, correct, and representative of all classes. While more data is good to get impartial results, it is crucial to make sure that the data collected is suitable for the problem at hand.

You can do this using population vs. sample. In this tutorial, you will learn all you need to know about population vs. sample.

## What is Population?

In statistics, population is the entire set of items from which you draw data for a statistical study. It can be a group of individuals, a set of items, etc. It makes up the data pool for a study.

Generally, population refers to the people who live in a particular area at a specific time. But in statistics, population refers to data on your study of interest. It can be a group of individuals, objects, events, organizations, etc. You use populations to draw conclusions.

Figure 1: Population

An example of a population would be the entire student body at a school. It would contain all the students who study in that school at the time of data collection. Depending on the problem statement, data from each of these students is collected. An example is the students who speak Hindi among the students of a school.

For the above situation, it is easy to collect data. The population is small and willing to provide data and can be contacted. The data collected will be complete and reliable.

#### Post Graduate Program in AI and Machine Learning

In Partnership with Purdue UniversityExplore Course

If you had to collect the same data from a larger population, say the entire country of India, it would be impossible to draw reliable conclusions because of geographical and accessibility constraints, not to mention time and resource constraints. A lot of data would be missing or might be unreliable. Furthermore, due to accessibility issues, marginalized tribes or villages might not provide data at all, making the data biased towards certain regions or groups.

## What is a Sample?

A sample represents the group of interest from the population, which you will use to represent the data. The sample is an unbiased subset of the population that best represents the whole data.

To overcome the restraints of a population, you can sometimes collect data from a subset of your population and then consider it as the general norm. You collect the subset information from the groups who have taken part in the study, making the data reliable. The results obtained for different groups who took part in the study can be extrapolated to generalize for the population.

Figure 2: Sample

The process of collecting data from a small subsection of the population and then using it to generalize over the entire set is called Sampling.

Samples are used when :

• The population is too large to collect data.
• The data collected is not reliable.
• The population is hypothetical and is unlimited in size. Take the example of a study that documents the results of a new medical procedure. It is unknown how the procedure will affect people across the globe, so a test group is used to find out how people react to it.

A sample should generally :

• Satisfy all different variations present in the population as well as a well-defined selection criterion.
• Be utterly unbiased on the properties of the objects being selected.
• Be random to choose the objects of study fairly.

Say you are looking for a job in the IT sector, so you search online for IT jobs. The first search result would be for jobs all around the world. But you want to work in India, so you search for IT jobs in India. This would be your population. It would be impossible to go through and apply for all positions in the listing. So you consider the top 30 jobs you are qualified for and satisfied with and apply for those. This is your sample.

## Differences Between Population and Sample

Now, try to understand what a sample and a population are, with the help of suitable examples.

Population

Sample

All residents of a country would constitute the Population set

All residents who live above the poverty line would be the Sample

All residents above the poverty line in a country would be the Population

All residents who are millionaires would make up the Sample

All employees in an office would be the Population

Out of all the employees, all managers in the office would be the Sample

Table 1: Population vs Sample

## How to Collect Data From a Population?

You collect data from a population when your research question needs an extensive amount of data or information about every member of the population is available. You use population data when the data pool is small and cooperative to give all the required information. For larger populations, you use Sampling to represent parts of the population from which it is hard to collect data.

Figure 3: Small Population: School final score analysis

An example of data collection over a small population is the analysis of the end-of-the-year marks. The schools need to collect the marks of all students and analyze their student's overall performance. As they only need to do it for the students in their school, they can use the entire population set.

Now consider the census data collection, which takes place every 10 years. The government news is to count all the people living in India. However, rural areas and tribal villages might not be accessible by the census agents, leading to marginalized communities being left out. The data collected from the census is used to allocate resources, so this negatively affects these communities.

Figure 4: Large Population: Census data collection

#### FREE Machine Learning Certification Course

To become a Machine Learning EngineerExplore Course

## How to Collect Data From a Sample?

Samples are used when the population is large, scattered, or if it's hard to collect data on individual instances within it. You can then use a small sample of the population to make overall hypotheses.

Samples should be randomly selected and should represent the entire population and every class within it. To ensure this, statistical methods such as probability sampling, are used to collect random samples from every class within the population. This will reduce sampling bias and increase validity.

Figure 5: Collecting random samples

Consider the polls conducted during election season to gauge the public support for various political parties all over the nation. It is impossible to ask millions of voters who their preferred candidate is, so they collect the opinions of a few hundred or thousand people from different sectors of the voting population.

That was all about population vs. sample.

Acelerate your career with thePost Graduate Program in AI and Machine Learningwith Purdue University collaborated with IBM.

## Conclusion

In this tutorial titled 'population vs. sample,' you look at what population and sample mean in statistics with the help of examples, some of the differences between population vs. sample You then looked at how data is collected from a population and a sample.

We hope this helped you understand what population and sample mean in statistics. To learn more about statistics and machine learning, check out Simplilearns Machine Learning Certification Course. If you have any questions or doubts, mention them in this tutorials comments section, and we'll have our experts answer them for you at the earliest!

Happy learning!

Ravikiran A S

Ravikiran A S works with Simplilearn as a Research Analyst. He an enthusiastic geek always in the hunt to learn the latest technologies. He is proficient with Java Programming Language, Big Data, and powerful Big Data Frameworks like Apache Hadoop and Apache Spark.

View More

Post Graduate Program in AI and Machine Learning

2937 Learners

Machine Learning Course

26104 Learners

Explore Category
Next Article

### Top Types of Sampling Techniques in Data Analytics

By Simplilearn
477
• DevOps Engineer Resume Guide

Ebook
• Whats the Difference Between Leadership vs Management?

Article
• Understanding the Difference Between Linear vs. Logistic Regression

Video Tutorial
• Data Scientist Resume Guide: The Ultimate Recipe for a Winning Resume

Ebook
• Know the Difference Between Projects and Programs

Article
• A One-Stop Guide to Statistics for Machine Learning

Video Tutorial
prevNext

Refer and Earn

Company

Work with us

Become an instructorBlog as guest

Discover

SkillupResourcesRSS feedSimplilearn Coupons and Discount OffersCity Sitemap

Corporate trainingPartnersDigital Transformation

Learn On the Go!

Get the Android AppGet the iOS App

Project Management Certification Course | Cyber Security Certification Course | Data Science Bootcamp Program | Data Analytics Bootcamp Program | Business Analysis Certification Course | Digital Marketing Certification Program | Lean Six Sigma Certification Course | DevOps Certification Course | Cloud Computing Certification Course | Data Engineering Course | AI and Machine Learning Course | Full Stack Web Development Course

Trending Master Programs

PMP Plus Certification Training Course | Big Data Engineer Course | Data Science Certification Course | Data Analyst Certification Course | Artificial Intelligence Course | Cloud Architect Certification Training Course | DevOps Engineer Certification Training Course | Advanced Digital Marketing Course | Cyber Security Expert Course | MEAN Stack Developer Course

Trending Courses

PMP Certification Training Course | Big Data Hadoop Certification Training Course | Data Science with Python Certification Course | Machine Learning Certification Course | AWS Solutions Architect Certification Training Course | CISSP Certification Training | Certified ScrumMaster (CSM) Certification Training | ITIL 4 Foundation Certification Training Course | Java Certification Course | Python Certification Training Course

Trending Resources

Python Tutorial | JavaScript Tutorial | Java Tutorial | Angular Tutorial | Node.js Tutorial | Docker Tutorial | Git Tutorial | Kubernetes Tutorial | Power BI Tutorial | CSS Tutorial