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  • Writer's pictureLaxman Parab

Sampling Methods in Statistics

Updated: May 11, 2023

What is Sampling?

The entire group that you want to draw conclusions about is called the population and the subset of population is known as sample.Sampling is the process of selecting a group of individuals from a population in order to study them and characterize the population as a whole. In statistics, sampling is when researchers determine a representative segment of a larger population that is then used to conduct a study.

Why sampling ?

  • Sometimes it’s simply not possible to study the whole population due to its size or inaccessibility.

  • Using sampling we can collect information faster and its also cost effective.

  • A smaller set of individuals often results in lesser data collection errors.

  • Storing and running statistical analyses on smaller datasets is easier and reliable.

Sampling Methods

There are two major types of sampling --- probability and non-probability sampling.

  • Probability sampling, also known as random sampling, is a kind of sample selection where randomisation is used instead of deliberate choice.

  • Non-probability sampling techniques are where the researcher deliberately picks items or individuals for the sample based on their research goals or knowledge.

Probability Sampling Methods

Here are some of the best-known probability sampling methods.

1. Simple Random Sampling:

In simple random sampling, we randomly choose a member from the population. Every member and set of member has an equal chance of being selected in the sample. It can be done with replacement (SRSWR) or without replacement (SRSWOR). This method is entirely based on chance.

Example: If you want to select a sample of 100 employees from a company with a population of say 1000 employees then by using simple random sampling you first assign a number to each employee from 1-1000 in the company's database and then draw 100 employees randomly from the database using any random generator tool

2. Systematic Random Sampling:

Systematic random sampling is similar to simple random sampling , but its usually easier to conduct. In this method we put a member of the population in some order and a starting point is choose as random the every 'nth' member is selected to be in a sample.

Example: Using the similar example from above, if we arrange the name of the emlpoyees according to the alphabetical order from A to Z. Choosing every nth member we do 1000/100=10 i.e.from the first 10 numbers, we randomly select a employee. If the number is 5, from 5 we select every 10th employee (i.e.5,15,25 and so on) and we finally obtain a sample of 100 employees.

3. Stratified Random Sampling:

Stratified sampling is used when the population has mixed characteristics and we want to ensure that every characteristics is proportionally represented in sample. In stratified random sampling we first divide the population into groups then from each group we select members randomly.

Example: If a company has 600 female employees and 400 male employees and we want a sample of 100 employees. So first, we split the population based on gender, then we use

random sampling and select the employees randomly from each group, selecting 80 female and 20 male give you a representative sample of 100 employees.

4. Cluster Random Sampling:

Cluster random sampling is used when natural groups are present in the population.

In cluster random sampling, we first divide the population into groups called clusters and then we randomly select the sample from all the clusters. What makes this different that stratified sampling is that each cluster must be representative of the population.

Example: Consider a scenario where an organization is looking to survey the performance of laptops across India. They can divide the entire country’s population into cities (clusters) and select further towns with the highest population and also filter those using laptop devices.

Non-Probability Sampling Methods

There are four types of non-probability sampling techniques: convenience, purposive, voluntary response and snowball sampling. Each of these sampling methods then have their own subtypes that provide different of analysis:

1. Convenience Sampling:

Convenience sampling is a common type of non-probability sampling where you choose participants for a sample, based on their convenience and availability i.e. the respondents who are easy to reach for the researcher.

2. Purposive Sampling:

In purposive sampling, we select a sample based on the purpose of the research. The researcher selects the sample by using their expertise and knowledge.

3. Voluntary Response Sampling:

Voluntary response sampling is based on the ease of access. The sampling members volunteer themselves instead of researcher selecting the participants and directly contacting them.

4. Snowball Sampling:

In Snowball sampling researcher recruit the participants via research participants for the test or study. It is used where it’s hard to find the potential population for research.



Probability sampling techniques are superior, but the costs can be prohibitive. For the initial stages of a study, non-probability sampling techniques might be sufficient to give you a sense of what you’re dealing with. While probability sampling is based on the principle of randomization where every entity gets a fair chance to be a part of the sample, non-probability sampling relies on the assumption that the characteristics are evenly distributed within the population, which make the sampler believe that any sample so selected would represent the whole population and the results drawn would be accurate.

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