Many people fail to understand the fundamental difference between the population and the sample. However, when analyzing data, knowing the difference between the two terms is vital.
- The population includes all members of a particular group, while a sample is a subset of the population selected for the study.
- Statistical sample analysis helps draw inferences about the population without examining every individual.
- Ensuring a sample is representative of the population is crucial for accurate results and generalizability.
Population vs Sample
The difference between population and sample is that the population includes all the units from a set of data. The sample includes a small group of units selected from the population. For example, a population may be all people living in Australia, and the sample may be a specific group of people living in Australia.
Another example could be that you want to check the number of people nearing retirement age in an organization.
Your population is the entire organisation’s workforce, whereas your sample could be employees older than 50 years old.
|Parameter of Comparison||Population||Sample|
|Definition||Population include the entire set of data. The size of the population depends on the scope of your research.||The sample includes data selected from the population. It is a subset of the population.|
|Measurable Quality||It is called a parameter.||It is called a statistic.|
|Advantages||Results could be more accurate when the entire population is used to carry out a study.||If the sample is representative of the population, reliable estimates could be made with less time and effort used.|
|Disadvantages||In most cases, it is impossible to test an entire population.||If the sample selected is not representative of the population, the results are not satisfactory.|
|Example||All kids registered in a school||Kids who got an A|
What is Population?
When we read the term population, we think of the people living in a country. However, when carrying out data analysis and comparing a set of data statistically, the world population has a different meaning.
A population includes all members of a specific group of data. For example, the mean age of women. This is a hypothetical population because it includes all women who have lived, is alive and will live in the future.
It is humanly impossible to test the entire population in the above scenario because not all members of the population are observable (e.g. women who will live in the future).
Even if it is possible to test the entire population, it will incur huge costs and a lot of time. Instead, we could use a subset of the population that is a sample. The sample helps to carry out a test on the above population and find the mean age of women.
For example, David is collecting data to know the meal preferences of the students in a school. When collecting data like David, it is vital to know the purpose of the entire population.
A population includes all the elements of data. For example, if David wants to collect information about all the students in his school, the population in this scenario would be all the students.
However, it is not practical to collect information from every population unit. When this happens, we have to find an alternate approach by obtaining information from a small group of members that represents the entire population.
David has the same issue; he cannot obtain information from every student in his school. Instead, He will need to get a sample.
What is Sample?
The sample contains a part of the population. The size of the sample is always less than the size of the population. A sample is the units of the data that actually participate in the study.
The question is, why use a sample and not the entire population?
- The population is too large in most cases and cannot be tested. For example, it is humanly impossible to test all the men in the world to find the mean height of the male population.
- The population is hypothetical. For example, we do not know the heights of the men who will live in the future.
- The population is not accessible in some cases. For example, certain tribes in the African jungles are still not known to the world, and hence they are not accessible.
For this reason, measurements are made on a subset of the population. If the samples are drawn effectively, the results obtained are as accurate as they would be if the measurements were made on the entire population.
The most commonly used sampling method is random sampling. Each sample is selected from the population on a random basis such that each item of the population has an equal chance of being selected. It is an unbiased sample and hence gives very effective results.
One of the most common methods of selecting a random sample is through the lottery method. Each unit of the population is given a random number.
The numbers are placed in a jar and adequately mixed. Then, a blindfolded person from the research team selects “N” numbers. The items of the population selected are included in the sample.
However, in some instances, it is impossible to carry out a random sample. In such cases, it is essential to consider the best alternative way to select the sample.
Main Differences Between Population and Sample
Before collecting any data and carrying out research, knowing the difference between population and sample is vital.
- A population includes all the elements of data, whereas a sample is a small part of the population that represents the entire population.
- The population is a complete set of data, whereas the sample is a subset of the population.
- Population-measurable quality is a parameter, whereas samples-measurable quality is a statistic.
- If the entire population is tested, results are a true representation of opinion, whereas if a non-representative sample is selected, results have a margin of error.
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Emma Smith holds an MA degree in English from Irvine Valley College. She has been a Journalist since 2002, writing articles on the English language, Sports, and Law. Read more about me on her bio page.