In the previous article, we got to know about what Population and what sample is. Now in this, we will learn about the various kinds of sampling techniques that are used in Data Science and statistics.
Important Term:
The population is denoted by (N)
The sample is denoted by (n)
Sampling Techniques are:
1. Simple Random Sampling:
In Simple Random Sampling suppose you have a population and sample from a dataset and you want to take a survey on that dataset. then what you'll do is randomly pick any sample from the population and do a survey on that sample. This kind of sampling is called simple random sampling in which you don't have to think anything but pick a random sample from the population.
Also when performing the Simple Random Sampling Every person in the population(N) has an equal chance of being selected for the sample(n).
2. Stratified Sampling:
Stratified Sampling is a technique in which where the Population (N) is split into non-overlapping groups. The non-overlapping group is also known as Strata.
Suppose you want to survey genders so in genders there are Male Groups and Female Groups the Gender groups can be further divided into age groups like 10-20, 20-30, 30-40 and 40-100.
In this we can say that Male Groups will do a different survey and Female groups will perform a different survey. Therefore the Male and Female cannot overlap each other in this scenario.
What's most important in Stratified Sampling is that the groups should not overlap each other.
Another eg we can take of stratified sampling is suppose we have
Dot net Developer
PHP Developer
Python Developer
Now in this, we can say that a Dot net developer may know Python and a PHP developer may know Dot net and Python also. so in this groups can overlap with each other.
But on the other hand, if you take eg such as
Engineer
Doctor
These groups can never overlap each other. Therefore these groups can be considered in stratified sampling.
3. Systematic Sampling:
Systematic Sampling is a very easy kind of sampling. In this sampling from the population, you pick up every nth individual and do the survey.
N --> nth individual
So suppose you're outside a school and you want to do a learning survey on the students.
Every 6th or 7th student you see. you will survey that student. In this survey there is no reason for selecting you just pick a number and survey that student.
4. Convenience Sampling / Voluntary Response Sampling:
In this suppose you are doing a survey so only those people who are domain experts in that survey will participate in that survey.
example: suppose you are surveying a data science subject so the people who are experts in Data Science will only participate in that survey.
These are 4 major types of Sampling Techniques in Statistics.