Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.
For enquiries call:
+1-469-442-0620
- Home
- Blog
- Data Science
- What are Sampling Techniques? Different Types and Methods
HomeBlogData ScienceWhat are Sampling Techniques? Different Types and Methods
Share
What are Sampling Techniques? Different Types and Methods
Published
07th Sep, 2023
Views
Read TimeRead it in
15 Mins
In this article
Data is the backbone of the majority of what researchers and data scientists do, and they require data to undertake experiments, analyze scenarios, and test ideas. The data samples come from the study population, and samples are the selected portion of the data that represent the whole population. However, dealing with enormous amounts of data is one of the main challenges in data analytics. It is unnecessary and even impractical to investigate the entire population when researching a specific group.
Data sampling is the process of analyzing data from a small group of individuals in a larger group. Data sampling allows you to research using various sampling techniques in data analytics without looking at the complete dataset. But what is the sampling technique? Essentially, it's the methods used to obtain a subset of data from a larger set for analysis. However, It is crucial for a person with a career to make sense of data, navigate it, and use it to impact a world filled with data. KnowledgeHut is an online platform focused on providing outcome-based immersive learning experiences to learners.
Now, build the required skills to learn data science and analytics with the Data Science Certificate online from KnowledgeHut and pursue your lucrative tech career seamlessly.
What is Sampling?
Statistics defines sampling as the process of gathering information about a population from a subset, like a selected individual or a small group and analyzing that information to study the whole population. The sample space constitutes the foundation of data which in turn is responsible for determining the accuracy of the study or research. Sampling, however, is not as simple as it seems. To land an accurate result, the sample size needs to be accurate, followed by implementing the right sampling methods based on the sample size. based on sample size.
Sampling Steps
An analyst needs to follow certain steps in order to reach conclusions from a broader perspective. The Sampling steps include the following -
- Step 1: Identity and clearly define the target group/population.
- Step 2: Create a specific sampling frame.
- Step 3: Select the right sampling methods to be used.
- Step 4: Specify the sample size.
- Step 5: Collect the required sampled data.
Major Types of Sampling Methods
There are two types of sampling methods used in market action research -
1. Probability Sampling
In the probability sampling approach, a researcher selects a few criteria and randomly selects individuals from a population. Using this selection parameter, each member has an equal chance of participating in the sample.
2. Non-Probability Sampling
In this type of sampling, randomly chosen participants are used by researchers. This type of sampling is not a set or predetermined selection procedure. As a result, it is difficult for all parts of a population to have equal chances of being included in a sample.
The expert-designed Bootcamp for Data Science can help you pursue your dream career in data science with comprehensive real-world skills and knowledge.
Different Types of Sampling Techniques
1. Probability Sampling
To choose and reach every unit in the population, probability sampling is typically favored when conducting large-scale investigations, particularly when a sample frame is available. We can measure the standard deviation of estimations, create confidence intervals, and formally test hypotheses using probability sampling.
The different sampling methods in probability sampling include:
A. Simple random sampling
Simple random sampling gives each member of the population an equal chance of being chosen for the sample. It's similar to drawing a name out of a bowl. Simple random sampling can be performed by anonymizing the population, for example, assigning a number to each object or person in the population and selecting numbers randomly.
Simple random sampling eliminates any bias from the sampling process and is inexpensive, simple, and quick to use. It also provides the researcher with no means of control, increasing the likelihood that unrepresentative groupings will be chosen randomly.
Applications
- Lottery techniques,
- Split and Train in machine learning.
Advantages
- Little bias due to the random nature of the sample collection
- Given the usage of random generators, sample selection is straightforward.
- Due to representativeness, the findings can be broadly interpreted.
Disadvantage
- All responders' potential availability might be expensive and time-consuming.
- Huge sample size
B. Cluster sampling
Cluster sampling involves selecting portions of the target population randomly from groupings rather than from single units. These might be already established groupings like residents of particular postal codes or students who attend a particular academic year.
However, in the case of a two-stage cluster sampling, the cluster can be randomly chosen in the first stage, and then the cluster can be randomly chosen again in the second stage.
Applications
- One Stage Cluster
- Two Stage Cluster
Advantages
- Reduces time and money.
- Practical and easy to use.
- Larger sample sizes can be used.
Disadvantage
- Increased errors in sampling
- The sample frame's variety might not be well reflected.
C. Systematic sampling
In systematic sampling, sometimes called systematic clustering, only the first item is subject to random selection. Afterward, every nth thing or person is chosen according to a rule. Although there is some element of randomness, the researcher may control the frequency at which things are chosen, ensuring that the picks won't unintentionally group.
Applications
- Quality Control: To statistically check the quality of their goods, industrial companies frequently utilize systematic sampling. Here, a sample is gathered by periodically grabbing something from the present production stream.
- Auditing: The most obvious method to sample an account list for an audit of savings accounts is to look for conformity with accounting processes.
Advantages
- Cost- and time-effective
- increases the sample's distribution across the population.
Disadvantage
- It is important to know the full population.
- Probable sample bias in case the dataset contains periodic patterns.
D. Stratified random sampling.
Stratified sampling uses random selection within established groupings. Knowing information about the target population helps researchers stratify it for research purposes. Although stratified sampling offers advantages, it also raises the issue of subdividing a population, increasing the chance of bias.
Applications
The three types of stratified random sampling are:
- Proportionate: When compared to the overall population, the sample size for each stratum in this method is proportionate to the number of the stratum's population.
- Disproportionate: A proportionate stratified random sampling differs from a disproportionate stratified random sampling only by its sampling fraction. Disproportionate sampling results in different sampling fractions for different strata.
- Optimal: Variable standard deviation determines the size of these strata in optimal stratified random sampling.
Advantages
- A greater percentage of all groups represented.
- The estimations can be as precise if there is uniformity within strata and variation across strata.
Disadvantage
- Complex methodology
- Possibly more costly and time-consuming
- Requires understanding of strata membership.
2. Non-Probability Sampling
Non-probability sampling techniques are selected when the precision of the results is not crucial. Non-probability sampling doesn't need a frame, is affordable, and is simple. The bias in the results can be lessened if a non-probability sample is appropriately implemented. Making assumptions about the entire population is hazardous to make, according to the fundamental drawback of non-probability sampling.
The different types of sampling techniques in non-probability sampling include:
A. Convenience sampling
The simplest sampling technique is convenience sampling, where participants are picked up based on their availability and desire to participate in the survey. The sample could not be representative of the population as a whole. Hence the results are subject to severe bias.
Applications
This type of sampling technique is usually conducted in offices and social networking sites. Example of sampling techniques includes online surveys, product surveys etc.
Advantages
- Obtaining a sample is comparatively easy.
- Cost-effective
- Participants are easily accessible.
Disadvantage
- Results cannot be generalized.
- Possibility of an imbalance in the population's representation
- Rise of substantial prejudice or biases in the sample frame
B. Judgmental or purposive sampling
In judgment (or purposeful) sampling, a researcher uses judgment to select individuals from the population to take part in the study. Researchers frequently think they can use good judgment to gather a representative sample while saving time and money.
There is a likelihood that the results will be extremely accurate with a small margin of error because the researcher's expertise is essential for establishing a group in this sampling approach.
Applications
This sampling method is used for a small group of chosen groups.
Advantages
- Relatively inexpensive and less time consuming
- Enables researchers to directly contact their target market.
- Near-real-time outcomes
Disadvantage
- Risk of the researcher making mistakes in judgment
- Bias levels are high, and dependability is low.
- Difficulty in generalizing study results
3. Snowball sampling
This sampling technique entails primary data sources proposing other prospective primary data sources that may be employed in the study. To create more subjects, the snowball sampling approach relies on referrals from the original participants. As a result, using this sampling technique, sample group members are chosen by chain referral.
When examining difficult-to-reach groups, the social sciences frequently adopt this sampling methodology. As more subjects who are known to the existing subjects are nominated, the sample grows in size like a snowball. For instance, participants can be asked to suggest more users for interviews while researching risk behaviors among intravenous drug users.
Applications
Three sub-parts of snowball sampling include -
- Linear snowball sampling - Only one subject is recruited, and the subject only makes one referral.
- Non-discriminatory exponential snowball sampling - One subject is recruited, and that one subject offers several references.
- Exponentially discriminative snowball sampling - One subject is recruited, who generates several references. However, only one topic is chosen from the recommendations.
Advantages
- Researchers can access uncommon subjects in a certain community.
- Inexpensive and simple to execute.
- The additional subjects can be recruited without the assistance of recruiting staff.
Disadvantage
- It's possible that the sample isn't representative.
- Bias in sampling might exist.
- It might be challenging to infer conclusions about the wider population with certainty since the sample is liable to biases.
D. Quota sampling
Quota sampling is the most used sampling technique used by most market researchers. The survey population is split up into subgroups that are mutually exclusive by the researchers. These categories are chosen based on well-known characteristics, qualities, or interests. The researcher chooses representative samples from each class.
Quota sampling is carried out in the following steps -
- Separate the population into distinct subgroups.
- Determine the percentage of auxiliary groupings in the population.
- Choose unique subjects for every group of subgroups.
- Make sure the sample represents the population.
Applications
- Controlled quota sampling - This limit the samples that researchers can choose by introducing certain restrictions.
- Uncontrolled quota sampling - In uncontrolled quota sampling, a researcher is allowed to select the individuals of the sample group.
Advantages
- Cost-effective
- Independent of sample frames
- Provides researchers with the opportunity to study a particular subgroup.
Disadvantage
- Possibility of an oversized sample
- Impossible to calculate the sampling error.
- Researchers' incompetence and/or lack of experience may lead to biases and substandard work.
Factors While Choosing Probability and Non-Probability Samples
To achieve the objectives of the study accurately, it is critical to pick a sampling technique carefully for every research project. However, it is important to note that different sampling methods require different elements to form the sample frame. The efficiency of the sample depends on several variables, which include types of sampling methods require different elements to form the sample frame. The efficiency of the sample depends on a number of variables, which include:
- To answer a research question, the sample size should be large enough but not so large that it becomes inefficient to sample.
- The margin of error.
- Depending on the study or use case, determine the best sampling method.
- Deviate from any pre-established sample guidelines to rule out biases.
- Omit hard-to-reach target groups.
- Low response rates.
Difference Between Probability Sampling and Non-probability Sampling Methods
The various sampling techniques in research and their subtypes have already been considered. To summarise the entire subject, however, the key distinctions between probability sampling techniques and non-probability sampling techniques are as follows:
| Probability Sampling | Non- Probability Sampling | |
|---|---|---|
| Sampling techniques definition | This is a sampling approach in which samples from a larger group are picked using a method based on probability theory. | This is a sampling strategy where samples are chosen by the researcher based on their own assessment as opposed to random selection. |
| Alternate Name | Random sampling | Non-random sampling |
| Nature | Conclusive | Exploratory |
| Selection of Population | Random selection | Arbitrary selection |
| Sample | Since this sampling is conclusive, it follows definitive sampling techniques. | Since this sampling is arbitrary, the sample representation is often biased. |
| Total time taken | Takes a longer time to complete because the research structure establishes the specified selection criteria before the market research investigation. | As none of the sample nor the sample's selection criteria are unstructured and ambiguous, this form of the sampling procedure is quick. |
| Results | Entirely unbiased and conclusive | Entirely biased and speculative |
| Hypothesis | Before the investigation ever starts, there is a guiding hypothesis, and the goal of probability sampling is to support that hypothesis. | In non-probability sampling, the hypothesis is created after the research study has been completed. |
Importance of Sampling
In data analytics, sampling is the process of selecting a representative subset of a larger population. Sampling is a relatively easier technique to study a population closely from drawn samples. "What is sampling techniques in research?" is a common question among budding researchers. Sampling helps an analyst determine a given population's characteristics more cost-effectively and practically.
Sampling aims to collect data that can be used to draw conclusions about the wider population. There are many reasons why sampling is important in data analytics -
- Sampling allows the data analysts to work with a smaller dataset, which can be more manageable and easier to work with.
- Sampling can help to ensure that the data is representative of the population as a whole, which is important because it means that the conclusions drawn from the data are more likely to be accurate.
- Sampling can help to reduce bias in data analysis. This is because Sampling allows analysts to select a representative subset of a population, which can help to reduce the skew that may be present in the data.
- Sampling is an important tool that helps analysts to collect accurate and reliable data.
Important Terminologies Need to Know
1. Population
The population in statistics is the entire group of items from which a sample can be drawn. The population can be defined in terms of geographical location, age, gender, occupation, etc. A population can be very large, making it impractical or impossible to study all group members.
2. Sample
A sample is a set of data collected and used to determine the population parameters. Once the sample has been selected, data can be collected and analyzed in order to make inferences or predictions about the population as a whole.
3. Sampling
Sampling is the process of selecting a representative group from a larger population. The main purpose of sampling is to provide information about a population that can be used to make inferences or predictions about that population.
Conclusion
Research and surveys require a sample from a large population. Sampling is a great way to achieve that. As many might wonder, "What is sampling techniques?", it's essential to know that sampling techniques vary depending on the type of results that one wants. It is important to remember that different sampling techniques should be applied according to the case taken; keeping this in mind, we must choose the appropriate sampling techniques. You would have a highly accurate (and time-consuming) undertaking on your hands if you could ask everyone in a population to participate in your study and have everyone respond. You would have a similarly accurate effort if you could ask every single person in a population to participate and have each one respond.
But since that isn't practicable, sampling provides a "good enough" option that trades some accuracy for convenience and usability. How well you account for bias, non-sampling error, and sampling error in your survey design will determine how much precision you lose. KnowledgeHut is a trusted online platform dedicated to skill-based learning modules.
Learn Machine Learning with Python, data science, and programming courses with KnowledgeHut’s expert-backed structure and start adding new feathers to your portfolio for that dream career in no time.
Frequently Asked Questions (FAQs)
1. Why is the sampling technique important?
In statistical analysis, sampling techniques meaning is the process of selecting a specified number of observations from a larger population. Depending on the sort of study being done, a variety of methods, including systematic sampling and simple random sampling, may be employed to draw samples from a broader population.
2. What are the limitations of sampling?
Certain limitations of sampling techniques include the following -
- Probability of biased results
- Selecting a good sample frame can be difficult at times
- Different sampling techniques demand different time frames
3. What is data sampling in data science?
Data sampling in data science is a statistical analysis approach used to pick, move, and analyze a representative selection of data points in order to spot trends and patterns in the larger data set being looked at.
4. What is your salary as a data scientist?
The salary range of a data scientist can be anywhere between ₹4 Lakhs to ₹24 Lakhs annually. The average salary of a data scientist in India is ₹11 Lakhs per annum.
5. Is data science a good career?
With the rapid growth of automation and technology in business modules, data scientists are very much in demand, which is projected to grow even more in the coming years. So it can be said that data science is a very promising career with tremendous growth opportunities shortly.
Upcoming Data Science Batches & Dates
| Name | Date | Fee | Know more |
|---|
Course Advisor
Hello! Ready to elevate your career? Explore 500+ future-ready courses with KnowledgeHut upGrad 🚀x
1
Fast forward your career with 500+ future-ready courses ⏩💻👩🎓📚x
1
