Difference between population and sample in research
Difference between population and sample in research

Article "Difference between population and sample in research" will explain population and Sample in research example.

Difference between population and sample in research.

In the scientific field, there are two types of data used for conducting the research i.e. the individual sample or the group sample. A population refers to a group that has been identified as such. The word population is derived from Latin populations which means all or every member of the society who belongs to a common category or a social class.


Difference between population and sample when we talk about public domain data:


This type of data are collected by any third party like government agencies or organizations i.e. Census Data etc.


This kind of data can be collected on large scale through a single location or many locations from multiple states etc.


Public Domain Data is like an open source data that is available by anyone to do their own work i.e. create their own analysis on this kind of data which helps them to solve their problems.


Public domain data includes both government and non-government organization data. Public domain data are also called data that is not under any legal responsibility from any authority.


Public domain data is just like open source that anyone can access without any permission. So, it is not protected, nor is it allowed to keep their privacy, so it’s important to consider this when choosing the database and software for your project.

Public domain data can be analyzed by many scientists at any point of time i.e. it doesn’t matter. It doesn’t have any restriction.


Public domain data is very easy to analyze and help other people solve their problem. When you have such data then you need to make sure that your project is using it correctly, otherwise you will face lots of issues.


Public domain data is useful for making any kind of analysis on them but if you use incorrect coding syntax then it can end up being bad for your project. Public domain data should be used only as per your requirement or analysis.


Public domain data can have many limitations which may include:


It doesn’t provide complete information about population e.g., age, income, occupation, gender, education, race and nationality etc.


Public domain data can be used for analyzing but it lacks some essential feature which is data cannot be stored in raw format, for e.g., text format, and therefore, they cannot be directly used in statistical experiments.

Public data is hard to share with outsiders because it contains personal information like identity number, credit card details, address, date of birth etc. This can get tracked easily by hackers so we need to put up measures to protect our sensitive data from unauthorized sources.


Public domain data can be limited only to specific region like country because it will be restricted. Government agencies or other governmental bodies can collect data on a larger scale but only on a particular state or few cities like Mumbai or Bangalore they can collect these data. But, sometimes one can get such data if we collect different kinds of demographic data. Such data should be only given to researchers who need to take care of it. To solve this problem it needs to be published in another source of the public domain data, where its availability and access can be easily regulated.

Public domain data also provides data which does not have any validity nor reliability. That is why Public domain data should be kept confidential and nobody shouldn’t try to interpret and analyze any part of its content.


Public data in itself is not safe at least. Even though there are lots of academic papers in front of us, we should think carefully and carefully about how much we are going to trust those papers because it might be biased and flawed. We can’t trust others unless we can trust ourselves first.

Public domain data is just like an open source. Anybody can access it for free.

Public domain data is very small scale. On a single geographical site or even several sites and can’t be found anywhere else. So, when selecting the database or package for your project you need to check for proper scalability. It is very important to select the right platform and tool for your project. There are many packages and tools available these days that are specially made for this type of projects. They are specially designed for big data projects like Google Analytics, Excel and Data Analysis and they are really user friendly and helpful.


Public domain data is very big when you want to conduct the analysis on a huge scale then Microsoft Azure is best for that. Azure always tries to offer its users everything as an open source so it offers full support, updates and security which doesn’t compromise with its functionality and performance.

In conclusion:


Public domain data is the most commonly used type of data in scientific field. While working on the dataset, you need to do certain thing about the data itself to see which kind of data it contains e.g., text, images, videos etc. As soon as you know what type your data consists of you can start thinking about how you should perform different analyses on this data. Once you know the information then start looking for related datasets as soon as possible and extract out relevant public domain data.


Public domain data is really a good starting point for researchers to choose and analyze other data. You need to be careful while using public domain data because it has all the features that can get corrupted easily. Therefore, you need to perform quality checks and find out whether the file is properly organized or not to avoid all sorts of errors. After all, it is a very important part of collecting the correct data.