Many people fail to understand the basic difference between the population and the sample. However, when analyzing data it is vital to know the difference between the two terms.
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 workforce of the organization, whereas your sample could be the employees who are older than 50 years old.
Comparison Table Between Population and Sample (in Tabular Form)
|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.||Sample includes data selected from the population. It is a subset of population.|
|Measureable Quality||It is called a parameter.||It is called a statistic.|
|Advantages||When the entire population is used to carry out a study results could be more accurate.||If sample is representative of the population reliable estimates could be made with less time and efforts 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 word 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, are 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 (for 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 important 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 in his school.
However it is not practical to collect information from every unit of the population. 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?
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 who 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, there are certain tribes in the African jungles that 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 method 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 properly mixed. Then, a blind-folded 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 important to consider the best alternative way to select the sample.
Main Differences Between Population and Sample
Before you collect any data and carry out research. It is vital to know the difference between population and sample.
- 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 called a parameter whereas samples measurable quality is called a statistic.
- If the entire population is tested results are true representation of opinion whereas if a non-representative sample is selected results have a margin of error.
To summarize, the sample is a small group of units who are selected from the population and will take part in the study and the population is the entire data on which the results will apply.
Carrying out measurements on the entire population is impossible in most cases and samples are selected to draw a conclusion about the population. However, for accurate results sample selected should be representative of the population.
Random Sample is the most commonly used sampling method and often provides the most representative sample.
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