Introduction
Sampling theory is one of the techniques of statistical analysis. When there is research conducted on a group of people, then it is barely responsible to manage the data of each individual. And there comes the relevance of sampling theory. In this article, we have brought our readers a detailed discussion on what is sampling theory and everything about it that one should know.
You can find the definition, sampling theory, and methods discussed in the latter part of this article. So stay tuned and enlighten yourself about the sampling theory and methods of it.
 Definition of Sampling Theory
 Difference Between Population and Sample
 Process of Sampling
 Types of Sampling
1. Definition of Sampling Theory
The sampling theory definition of the statistic is the creation of a sample set. This is recognized as one of the major processes. It retains the accuracy in bringing out the correct statistical information. The population tree is a huge set and it turns out to be exhausting for the actual study and estimation process. Both money and time get exhausted in the process. The creation of the sample set saves time and effort and is a vital theory in the process of statistical data analysis.
2. Difference Between Population and Sample
Once you know the difference between these two terms, you are eligible to understand the information laid ahead. In this part, you will learn how to identify the population and sample. The population can be referred to as the whole group of which you want to have a conclusion after making a statistical analysis. Samples are the groups within a population from which the data are to be collected.
The population can be categorized in terms of geographical locations, income, age groups, and many other categories. The population can be a narrow or a broad group as per the requirement. It would appear clearer with sampling theory examples. For instance, if you are willing to conclude making statistical analysis about a topic on the adults, then the population can be a huge broad group. And on the other hand, if you are researching a particular company, then the population is a narrow one. The whole set of elements or entities is referred to as Population.
The sample frame is nothing but the set of sample elements that are under observation. The creation of samples is all that defines sampling theory. This is precisely the set of people that will be actively participating in the process of statistical analysis. The sampling model is when the set of the population has infinite elements.
3. Process of Sampling
In this part of the article, we will discuss a few details regarding the process of sampling. So the steps are mentioned in the steps below:
 The first step is a wise choice of the population set.
 The second step is focusing on the sample set and the size of it.
 Then, one needs to choose an identifiable property based on which the samples will be created out of the population set.
 Then, the samples can be chosen using any of the types of sampling theory – Simple random, systematic, or stratified. Each of them is thoroughly discussed in the article ahead.
 Checking the inaccuracy, if there is any.
 Hence, the set is achieved in the result.
4. Types of Sampling
Sampling can be done in their different methods and they are briefly discussed in the pointers below:
A) Simple Random Type
This one is known to be one of the most elementary processes of sampling. This method is divided into sets equally. The random sets can also have no defining identifier in them. Hence, choose your population set. Select the sampling and then randomly pick one element from each set. The pros of the methods are it is less timeconsuming, it is done with less number of elements and this method of sampling can be conducted anywhere in any given time as they do not require random generators.
B) Systematic Sampling
Systematic sampling is otherwise known as probability sampling. It is more accurate than that of simple random sampling. The error in the process is quite low and is negligible. Based on an order, the elements in the population set are arranged and the process is known as sorting. You can arrange the elements of the population into any order and perform the statistics. So they can be kept in ascending or descending or any other order. And the starting point should always be random. But the statistic is performed on the predefined function.
Hence the methods involve choice of population wittily, checking on whether the systematic sampling can be done wisely or not, if yes, then sorting the elements well, and then as per the function crawling out elements. By following this method, the accuracy is higher and the probability of errors is comparatively low than that of SRS.
C) Stratified Sampling
This process of sampling is known to be one of the fussiest and complex method out of all the others. This is a hybrid method or a third way that is derived out of the above two methods and involved both of them. This method is considered to be advanced and most soughtafter one. It also provides accurate results. In this method, all the elements are divided into stratum. Each stratum will have its identifiable properties. After this division, simple random sampling or systematic sampling can be done to pick up performing samples out of them.
The method to conduct this sampling is choosing the set of population wisely. Checking on identifiable properties and dividing the population based on that. The division of the population will be into strata based on the property that is unique for the group. Then using either of Simple Random method or systematic sampling to form a sample. And then the last step is again following the same sampling method for the subsets created in the above step.
The accuracy hence achieved in the process is out of measurements. Results can be different if the sampling method is altered. The reason behind the accuracy of the process is it also analyzes the strata, creating substrata, which is again analyzed through desirable sampling methods to gain results. The only case where the process might fail is when the elements are homogeneous.
Final Words
The concept of sampling has a huge implementation and its application is seen in many vital fields like communication. Sampling theory is a vital theory and all the above information is richly packed up with important data about sampling theory.
The importance of sampling theory is when it comes into play while making statistical analysis. With different efficiency levels, there are three different methods of sampling. We have adequately thrown light on the process and methods of sampling. Here is hoping that we have provided with you enough information about sampling theory.
If you are interested in making a career in the Data Science domain, our 11month inperson Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional.
Also Read
PEOPLE ALSO READ

PotpourriJigsaw Academy is the #1 Analytics Training Institute in India

ArticlesKnow How Rajath Made 2021 The Year Of Upskilling With Our PG Certificate Program In Product Management!

Salaries in AnalyticsDiscover, Learn And Prepare For The Era Of Product Management!

ArticlesWonder What The Right Foundation Can Do To Your Data Science Career?

ArticlesZero Prior Knowledge Will Not Stop You From Reinforcing Your Career With Data Science!