What are Sampling Techniques? Types and Methods

Getting your Trinity Audio player ready...

What is Sampling?

The practise of picking a subset of the information from a larger set of information to analyse and make implications about the whole population is referred to as sampling in the field of data analysis. It includes selecting representative numbers that capture the essential features of the larger dataset, allowing analysts to come to conclusions. Furthermore, they can draw insights without studying the entire collection of data.

When it is unattainable or time costly to analyse the whole population, sampling techniques in Data Analytics is is widely utilised. Analysts can save precious resources and time by employing fewer samples while nevertheless getting significant results. Proper methods of data sampling process are essential in ensuring that the samples chosen correctly reflect the population while avoiding bias.

Different Types of Data Sampling Techniques:

Probability sampling and non-probability sampling are two distinct approaches to selecting samples from a population. Opting for a Data Science Job Guarantee program by Pickl.AI may help you learn both the sampling techniques effectively.  Here are the key differences between these two methods:

Probability Sampling Techniques:

  • Simple Random Sampling: This technique involves randomly selecting individuals or items from the population, where each member has an equal chance of being chosen. It is one of the most straightforward sampling methods, ensuring that every unit in the population has an equal likelihood of inclusion. A simple random data sampling example would be assigning a number to each person in the population and selecting random numbers.
  • Cluster Sampling: Cluster sampling involves dividing the population into clusters or groups and randomly selecting entire clusters as the sample. It is often used when the population is geographically dispersed or when it is more practical to sample clusters instead of individual units.
  • Systematic Sampling: Systematic sampling involves selecting every kth element from the population after a random starting point is determined. For example, if the population size is N and a sample size of n is desired, every N/nth element is selected.
  • Stratified Sampling: In stratified sampling, the population is divided into subgroups or strata based on certain characteristics. Samples are then randomly selected from each stratum in proportion to their representation in the population. This method ensures that each subgroup is adequately represented in the sample.

Non-Probability Sampling Techniques:

  • Convenience Sampling: Convenience sampling involves selecting individuals or items that are easily accessible or convenient for the researcher. This method is used when time, cost, or resources are limited. However, convenience sampling may introduce bias and may not represent the entire population accurately.
  • Snowball Sampling: Snowball sampling is used when the target population is hard to reach. It involves selecting initial participants and then asking them to refer others who meet the criteria. The process continues, with the sample size growing like a snowball.
  • Quota sampling: Quota sampling is a non-probability sampling technique used in research to gather data from a specific subgroup of a population. It involves selecting individuals to participate in a study based on pre-defined quotas or specific characteristics, rather than using random selection. In quota sampling, the population is divided into mutually exclusive subgroups, known as quotas, based on specific criteria such as age, gender, ethnicity, occupation, or any other relevant characteristic.

Each sampling technique has its advantages and limitations, and the choice of method depends on factors such as the research objectives, available resources, and characteristics of the population being studied.

Sampling Methods

Difference Between Probability Sampling and Non-probability Sampling Methods

Probability sampling and non-probability sampling are two distinct approaches to selecting samples from a population. Here are the key differences between these two methods:

Probability Sampling:

Probability sampling involves a selection process where each element in the population has a known and non-zero probability of being included in the sample. The sample is selected based on the principles of randomness and equal chance of selection.

  • Representativeness: Probability sampling aims to create a sample that is representative of the population, meaning that the characteristics and proportions of the sample closely resemble those of the population.
  • Sampling Methods: Common probability sampling methods include simple random sampling, stratified sampling, systematic sampling, and cluster sampling.
  • Generalization: Probability sampling allows for statistical generalization. The findings from the sample can be generalized to the population with a known level of confidence.
  • Sampling Error: Probability sampling enables the estimation of sampling error, which measures the variability between the sample and the population. Statistical techniques can be applied to quantify and account for sampling error.

Non-probability Sampling:

Non-probability sampling involves a selection process where the probability of any particular element being included in the sample is unknown or intentionally not equal for all elements. The sample is typically selected based on convenience or judgment.

  • Representativeness: Non-probability sampling does not guarantee representativeness. The sample may not accurately reflect the characteristics or proportions of the population.
  • Sampling Methods: Common non-probability sampling methods include convenience sampling, purposive sampling, quota sampling, and snowball sampling.
  • Generalization: Non-probability sampling does not support statistical generalization. The findings from the sample cannot be reliably generalized to the larger population.
  • Sampling Error: Non-probability sampling does not allow for the estimation of sampling error. Since the sample selection process lacks a known probability distribution, it is not possible to measure the sampling error.

While probability sampling provides a foundation for statistical inference and generalization, non-probability sampling methods are often used when it is difficult or impractical to implement probability sampling techniques. Non-probability sampling is commonly employed in qualitative research, exploratory studies, or situations where the emphasis is on understanding specific cases or capturing diverse perspectives rather than statistical representation.

Factors While Choosing Probability and Non-Probability Samples:

There are important factors in the sampling process, but they are not always distinct kinds of the sample procedures. Choosing the right the sample method is important in obtaining reliable and reliable data, and investigators ought to carefully weigh these elements while developing their method of sampling strategy.

  • Sample Size: Based on the research targets, desired level of exactness, and available resources, an appropriate sample size needs to be chosen. A greater number of participants yields estimations that are more precise in general, but it may become costly and computationally demanding to produce.
  • Margin of Error: This is the allowed amount of sampling error or unpredictability in the estimates. A bigger sample size needs to be used for a smaller degree of error.
  • Selecting a Sampling Method: The sample approach used is determined by an assortment of criteria, notably the research objectives, background information, and available resources. As was previously noted, different techniques for sampling have different strengths and limitations.
  • Avoiding Bias: To guarantee that the number of participants is accurately representing the population, it is important to minimise bias in the sampling process. Non-random selection, non-response, or under-representation of particular demographics can all result in bias. Researchers ought to implement precautions to reduce bias and ensure that the population being studied is as unbiased as possible.
  • Difficult-to-contact Population Groups: Some populations can prove difficult for researchers to reach or included in the sample. It is critical to investigate different strategies or tactics for including these groups for the purpose to ensure that the number of participants is representative and free of biases.
  • Response Rates: Low rates of response are susceptible to non-response bias, in which non-respondent features differ from those of respondents. Researchers ought to attempt to maximise response rates by interacting and connecting with the sampled individuals successfully.


The blog has been effectively able to explain the importance of Sampling techniques and the valuable tools in data analysis that ensures researchers to derive meaning out of the data. By understanding the methods of sampling and the key factors of sampling process, researchers can enhance the effectiveness of their studies.


1.  What is Data Sampling in Data Science?

Data Sampling is a technique of statistical analysis useful for selecting, manipulating and analysing a subset of data points to identify patterns and uncover meaningful information.

2.  Why is the Sampling technique important?

The importance of Sampling is understood in terms of the need to provide statistical data on an extensive range of subjects for purposes of research and administration.

3.  What are the limitations of sampling?

There are various limitations to sampling which includes the following:

  • Sample survey is unsuitable in case higher accuracy is required.
  • The conclusions of the analysis will not be correct if the items in the sample are not selected unbiasedly.
  • Investigator’s personal bias towards selection of units and drawing samples may lead to false outcomes. 


  • Ayush Pareek

    Written by:

    I am a programmer, who loves all things code. I have been writing about data science and other allied disciplines like machine learning and artificial intelligence ever since June 2021. You can check out my articles at pickl.ai/blog/author/ayushpareek/ I have been doing my undergrad in engineering at Jadavpur University since 2019. When not debugging issues, I can be found reading articles online that concern history, languages, and economics, among other topics. I can be reached on LinkedIn and via my email.