Population and sample are components of a survey. During a survey, you collect data to reach a sound and clear solution for your research. The method of collection of data is important as it enables the researcher to take decisions related to the information available and to ensure accuracy. A population is the entire group, having at least one common feature, that is chosen to draw conclusions about whereas a sample is the specific group that you will collect data from. The size of the sample will always be less than the total size of the population. Let us understand about population and sample in detail.
Looking for a Data Science Career? Explore Here!
What is Population?
Population, in general, refers to the people who live in a particular area at a specific time. In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organisations, countries, organisms, etc. The number of elements or members in a population is known as population size. Population includes all the elements from the data set. The measurable characteristics of the population such as mean and standard deviation are known as a parameter.
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.
The different types of population are:
Finite Population: When the number of elements of the population is countable thus making it possible to enumerate it in totality, the population is said to be finite. An example is the number of shops in a shopping mall.
Infinite Population: When the number of units in a population are uncountable, and hence becomes impossible to observe all the items, then the population is considered as infinite. An example is the number of germs in our body.
Existent Population: The population which comprises of objects that really exists is called existent population. An example is the number of books in a library.
Hypothetical Population: Hypothetical or imaginary population is the population whose unit is not available in solid form. An example is the outcome of rolling a dice.
Collecting data from a large population is not feasible as the data might be inaccurate due to several reasons.
What is Sample?
A sample is a small portion of a large group randomly chosen for participation in the study. It is used in statistical testing when the population size is too large for all the elements to be included in the survey. A sample should satisfy all the variations in the population. The results obtained for different groups who took part in the study can be used to draw a conclusion for the population.
The process of selecting elements for a survey is known as sampling. The units under study are called sampling units, and the number of units in a sample is called sample size. Samples should be randomly selected and should represent the entire population and every class within it. It should be free from bias and the investigator should not exercise his choice preference.
There are two types of sampling. They are:
- Probability sampling: In this type of sampling, you follow certain procedures to select the sample. This is to ensure that every unit of the population consists of one fixed probability being included in the sample.
- Non-probability sampling: This type of sampling has no theoretical basis for selecting the sample. It is done at the discretion of the researcher.
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.
Examples of Population and Sample
Population | Sample |
The number of people in a country | The female population of the country |
All the students in a class | The top ten students |
The number of workers in a factory | The number of managers in that factory |
All the doctors in a hospital | The number of cardiologists in that hospital |
The books in a library | The number of Hindi books |
Join Our Data Science and Machine Learning Course! Enroll Here!
Differences Between Population and Sample
Population | Sample |
The collection of all elements having common characteristics is known as the population. | Sample is a subset of the population which includes members chosen for participation in the study. |
The characteristic of population based on all units is called parameter | The measure of sample observation is called statistic. |
Process of collecting information from a population is known as census. | Survey conducted to gather information from the sample using sampling method. |
The focus is to identify the characteristics of the elements | The focus is on making the generalisation about the characteristics of the population, from which the sample was chosen. |
This article gives you a picture about what population and sample means in statistics and how they are chosen. A population is a group of elements that have something in common whereas sample is the group of individuals or elements that have participated in a survey.
In spite of all the differences, sample and population are interrelated, i.e. sample is drawn from the population, so without population sample may not exist. The primary objective of the sample is to make statistical inferences about the population that too would be as accurate as possible. The greater the size of the sample, the higher is the level of accuracy of generalisation.
Frequently Asked Questions (FAQs)
Why are samples used in research?
Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.
When are populations used in research?
Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.
Related Articles |
Discussion about this post