STATISTICS • BASIC CONCEPTS
What Are Population and Sampling in Statistics?
In statistics, we often want to learn something about a large group of people or things. Studying every member can take too much time and money, so we may study a smaller group instead.
Two important words for this idea are population and sample. The process of choosing that smaller group is called sampling.
1. What Is Population in Statistics?
A population is the complete group of people, objects or items that we want to study. It includes everyone or everything that fits the purpose of our study.
Example: Suppose a school wants to know the favourite subject of its students. If the school has 800 students, all 800 students form the population for this study.
- To study the height of students in a class, all students in that class are the population.
- To study the quality of bulbs made in a factory during one day, all bulbs made that day are the population.
- To study the marks of Class 10 students in a school, all Class 10 students in that school are the population.
The population does not always mean people. It can also mean books, trees, products, test results or any other group we want to study.
2. What Is a Sample?
A sample is a smaller group selected from the population. We study this smaller group to learn about the larger group.
Example: The school has 800 students, but asking every student about their favourite subject may take time. The school selects 80 students and asks them the question. These 80 students are the sample.
A sample should be chosen carefully. If it represents the population well, it can help us make a useful estimate about the whole group.
3. What Is Sampling?
Sampling is the process of selecting a sample from a population.
In simple words, population means the whole group, sample means the selected part, and sampling means the method used to select that part.
4. Population vs Sample vs Sampling
| Term | Meaning | School example |
|---|---|---|
| Population | The complete group being studied | All 800 students in the school |
| Sample | A smaller group selected from the population | 80 selected students |
| Sampling | The process of selecting the sample | Choosing which 80 students to ask |
5. Why Do We Use Sampling?
Sampling is useful because studying every member of a large population can be difficult. A carefully selected sample can save time, effort and money.
- Saves time: We can collect information from fewer people or items.
- Costs less: Fewer interviews, tests or inspections may be needed.
- Makes large studies manageable: A sample is often easier to organise and study.
- Helps with quality checks: A factory can inspect selected products to check how its production is going.
However, a sample may not give a good picture of the population if it is chosen unfairly or is too small for the purpose.
6. Common Types of Sampling
There are several ways to select a sample. Here are a few common methods in simple language.
Random sampling
Members are selected by chance, so each member in the defined population has a known chance of being selected. For example, drawing student names from a box.
Systematic sampling
After choosing a starting point, we select every fixed-numbered member. For example, selecting every 10th name from a suitable list.
Stratified sampling
The population is divided into important groups, and samples are taken from each group. For example, selecting students from every class or grade.
Convenience sampling
People or items that are easiest to reach are selected. It is simple, but the sample may not represent the whole population well.
7. Easy Example to Remember
A city has 20,000 households. A researcher wants to learn how many households use solar energy.
- Population: All 20,000 households in the city.
- Sample: The 500 households selected for the survey.
- Sampling: The process used to choose those 500 households.
If the selected households fairly represent the city, the researcher can use their answers to estimate solar-energy use across the city. The result is an estimate, not a guarantee that every household has the same pattern.
8. Common Mistakes
- Calling the sample the whole population. A sample is only part of the population.
- Using the words sample and sampling as if they mean the same thing. A sample is the selected group; sampling is the selection process.
- Assuming any sample represents the population well. The method of selection matters.
- Thinking that a larger sample is always fair. A large sample can still be biased if it leaves out important parts of the population.
Quick Revision
- Population: The complete group we want to study.
- Sample: A smaller group selected from the population.
- Sampling: The process of selecting the sample.
- Purpose: To learn about a large group with less time, effort and cost.
- Important point: A good sample should represent the population as fairly as possible.
Frequently Asked Questions
What is population in statistics?
Population is the complete group of people, objects or items that a study is about.
What is a sample in statistics?
A sample is a smaller group selected from the population to collect information and learn about the larger group.
What is sampling?
Sampling is the process of selecting a sample from a population.
Why is sampling important?
It can make a study quicker and less expensive, especially when the population is very large.
Can a sample give a wrong result?
Yes. If the sample is too small for the study or is selected in a biased way, it may not represent the population accurately.