Methods of Sampling

Sampling is like studying a small piece to understand the whole. Instead of collecting information from every single person or item, which can be tiring, expensive, and sometimes impossible, we select a smaller group that represents the larger population.

It’s a bit like taking a small bite of a cake to know how the whole cake tastes.
If the chosen sample is fair and well selected, the results from that small group can tell us a lot about the entire population.

Example :

Think of a school with 1,000 students.
The principal wants to find out how many students prefer online classes to offline classes.

But asking all 1,000 students would take a long time.
So instead, the principal picks only 100 students from different classes to ask the question.

These 100 students = Sample

All 1,000 students = Population

It’s like tasting a spoon of soup to know the whole bowl’s flavour.
 

Now, let’s learn about the types of sampling that can be classified into probability sampling and non-probability sampling. We will now learn the Types of sampling methods in Statistics, including probability sampling and non-probability sampling along with their types.

Here, the samples are randomly selected from the data set. Each entity of such a population has an equal chance of being selected to be part of the sample. The types of probability sampling are:

• Simple Random Sampling 
   
• Cluster Sampling 
   
• Systematic Sampling 
   
• Stratified Random Sampling 
  
   

Simple Random Sampling

When the members of the population have an equal chance of being selected, it is known as simple random sampling. It is purely based on chance. It is the most direct method of probability sampling.

Cluster Sampling

In this type of sampling, the population is divided into subgroups, which are known as clusters. The sample is selected from each cluster or group, not from the population. 

Systematic Sampling 

In this type of sampling, the population is given a number. The first individual is selected randomly, and the other is selected from the fixed intervals.


Stratified Random Sampling 

Here, the population is divided into subgroups based on traits such as age, gender, category, and so on. And from each group, we select samples.

 

In this method, the samples are selected based on certain criteria; it is a non-random sampling technique. The types are:

• Convenience Sampling
  
   
• Quota Sampling
  
   
• Judgment Sampling 
  
   
• Snowball sampling 

Convenience Sampling

In this method, the data collection is dependent on their ease of access. Here, the sampling is used when initial data needs to be collected inexpensively. 

Quota Sampling

In this sampling method, the data is collected based on the predetermined characteristics of the population. In quota sampling, the sample cannot be the best representation of the characteristics of the population.  

Judgment Sampling 

Here, the sample is selected based on the judgment of the experts. The experts choose the participants. It is also known as selective sampling. 

Snowball Sampling 

Here, a few people are asked to nominate more people known to them, so the sample size increases. This tends to have a snowballing effect on the sample size.
 

Now let’s learn the step-by-step process of sampling.  

Step 1: Identify and define the target population 

In this step, the researcher will identify and define the target population. 

Step 2: Selection of sample frame

In this step, the researcher will do the sampling frame, that is, they will create a list of people or items of the population from which the sample is taken.

Step 3: Selecting the sample method

Here, based on the type of population, the researcher will decide on the sampling method such as probability sampling or non-probability sampling, and so on. 

Step 4: Choosing the sample size

We decide the sample size based on the population, that is, the sample size should be able to make inferences about the population. 

Step 5: Collecting the data

After all these processes, we will have a perfect sample for the research, that is, now we are left with only collecting data. 
 

Do you know the differences between population and sample? If not let’s learn it together, here are the differences.
 

Now let’s learn about the advantages and disadvantages of data sampling methods.

Sampling becomes much easier to understand when we follow a few smart and practical tips. These ideas help us select a sample that is fair, meaningful, and representative of the population as a whole. While learning this, it is also helpful to know the different sampling methods, how each works, and when to use them in real-world situations.
 

Choose a sample that represents everyone: The people or items selected should not all be similar. If we want to study students in a school, we should include students from many grades rather than rely on a single class. This is why understanding the comparison of statistical sampling methods is useful it helps us decide the best method for different types of data and situations.
 

Don’t pick only the easiest options: Choosing only familiar people or nearby groups may lead to unfair results. A good sample must include different opinions, abilities, and backgrounds. Methods under probability sampling are especially helpful here because they give everyone an equal chance of being selected.
 

Use random selection whenever possible: Random sampling is like picking names from a bowl without looking. It reduces bias and makes data more trustworthy. Many probability sampling methods follow this rule and are often used when accurate, scientific results are needed.
 

Choose a sample size that is neither too big nor too small: If the sample is too tiny, it may not show the true picture of the whole group. A huge sample can also take a lot of time and effort. In surveys, especially in qualitative research, selecting a balanced sample size helps ensure honest opinions and realistic responses.
 

Teachers and parents play a guiding role: Teachers can help students learn how to pick fair and meaningful samples for projects and classroom surveys. Parents can encourage children to include people from different backgrounds, rather than choosing only friends, helping them collect more reliable and complete results.

In various fields, we use sampling to collect information from an entire population. Let’s discuss a few real-world applications.

• In marketing we use sampling to conduct research on market trends, customer preferences, purchase behavior, and so on. 
  
   
• Researchers use sampling techniques to study the nature and pollution level.
  
   
• In factories, random sampling methods are used to inspect the small batch of products to identify defective products.
  
   
• To improve the curriculum and teaching methods, student feedback surveys are used.