Different Types Of Sampling In Statistics: You should Know

As we know statistics is an essential part of our daily lives. There are various uses of statistics that we apply in our day-to-day life. Weather forecasting is one example of statistics. But here the question is what are the types of sampling in statistics? But before that do you know what sampling is? If yes, then it is great. But, if not, then do not worry. Many students are not aware of the types of sampling in statistics. 

 

In simple words, sampling is the process to estimate the total population by using a dataset of the population. There are different types of sampling in statistics that we will discuss in this topic. So, keep scrolling to know more.

Before discussing the types of sampling in statistics, we will understand what sampling is.

What is Sampling?

In statistical analysis, sampling is the selection of a particular number of observations from a big population. 

Key Points

 

• During audits, Certified Public Accountants use sampling to check the correctness and completeness of account balances.
• Random sampling, block sampling, judgment sampling, and systematic sampling are all types of sampling.
• Companies use sampling as a marketing technique to figure out what their target audience wants and needs.

Types of Sampling In Statistics

There are two different types of sampling in market research: probability sampling and non-probability sampling. Let’s take a deeper look at these two sample strategies.

Probability Sampling

Probability sampling is a sampling method in which a researcher chooses members of a population randomly. It is based on a set of methods. With this selection method, whole members have an equal chance of being included in the sample.

Types of Probability Sampling

Probability sampling methods are divided into four categories:

 

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

Uses of Probability Sampling

Probability sampling has a variety of uses:

Diverse Population

When the population is large and complex, proper representation is necessary. It is important to ensure that the data is not biassed towards one group. 

Reduce Sample Bias

The bias in samples made from a population is low to non-existent when using the probability sampling method. The researcher’s ideas and assumptions are mostly reflected in the sample selection. Because the sample accurately represents the population, probability sampling results in higher-quality data collection.

Create An Accurate Sample

Researchers can use probability sampling to plan and construct an accurate sample. This helps in the collection of well-defined data.

Non-Probability Sampling

The researcher selects members for research randomly in non-probability sampling. This sampling method is not a planned or set procedure. 

Types Of Non-Probability Sampling

The four types of non-probability sampling are;

• Convenience Sampling
• Purposive Or Judgmental Sampling
• Snowball Sampling
• Quota Sampling

Uses Of Non-Probability Sampling

 

Followng are the uses of non-probability sampling:

Exploratory Research

When performing qualitative research, pilot studies, or exploratory research, researchers frequently use this sampling method.

Budget and Time Restrictions

 

Create A Hypothesis

When there is little or no previous knowledge available. Researchers use the non-probability sampling method to make an assumption. This strategy helps in the quick delivery of data. While also establishing a foundation for future study.

 

Distinguish Between The Two Types Of Sampling In Statistics: Probability Sampling And Non-Probability Sampling

 

Nature

 

Probability Sampling: The findings are conclusive.

 

Non-Probability Sampling: The study is exploratory in nature

Sample

Probability sampling: The features of the population are accurately represented as there is a process for selecting the sample.

 

Non-Probability sampling: Because the sampling process is random. 

Population Selection

 

Probability Sampling: The population is chosen at random.

 

Non-Probability Sampling: The population is randomly chosen.

Time Taken

Probability Sampling: It takes longer to perform. 

Non-Probability Sampling: Because neither the sample nor the sample selection criteria are unknown. This sampling approach is fast.

Results

Probability Sampling: This form of sampling is completely unbiased.

Non-Probability Sampling: This form of sampling is completely biased.  It makes the research questionable.

Hypothesis 

Probability sampling: Before the investigation, there is an essential hypothesis. The goal of probability sampling is to prove the theory.

Non-Probability Sampling: The hypothesis is formed after the research investigation is completed.

 

To accomplish the study’s objectives, it is critical to use the right sample strategy for your research. Here are some of the steps that professional researchers use to choose the appropriate sampling strategy.

 

• Make a list of your research objectives. In several cases, it has to be a mixture of price, precision, and accuracy.
• Determine the most effective sampling methods for achieving the study objectives.
• Examine each of these methods to see if they help you in accomplishing your objective.
• Choose the research method that works best.

Conclusion

To sum up, we have discussed the types of sampling in statistics. So, the two main types of sampling in statistics are probability sampling and non-probability sampling. Both are important types and different from each other. That is why we have also discussed some points of difference among them. Some people are not able to choose an accurate sampling method. Therefore, in the above blog, you can learn how you can decide which type of sampling in statistics should be best.