Probability And Non-Probability Sampling: Definition And Key Difference 

Sampling is an integral part of the research, and sampling strategy is a cornerstone of the process. Sampling techniques fall under two primary subcategories: probability and non-probability sampling. In the first scenario, each participant has a fixed, known probability of becoming a part of the sample, whereas, in the latter, there is no such guarantee. 

The layman may perceive these two ideas as the same, but in reality, they are distinct. We’ll dig deeper into other crucial differences between probability and non-probability sampling. 

What Does Probability Sampling Entail? 

A probability sample is a sample that has an equal chance of including every member of the population based on predetermined criteria. Based on the randomization principle, the procedure is structured to ensure that every member of the population has a fair chance for selection. Thus, the possibility of bias is reduced. 

Using this method, researchers can draw statistical conclusions, i.e., extrapolate the findings from the sample they surveyed to the intended audience. It is often employed when the population has predictable and quantifiable components. Additionally, it is primarily utilized in quantitative research to ensure accuracy. Here are a few methods for probability sampling: 

  • Simple random sampling gives each element in a population the same chance of being included in the sample. Hence, the chances of it being selected are the same.  
  • The systematic sampling method involves listing items and drawing samples from the list at fixed intervals.  
  • A stratified sampling method divides the population into non-overlapping groups, called strata, and samples are chosen randomly from each stratum.  
  • Cluster sampling involves categorizing a population into groups and selecting a group or cluster representing a large sample.  
  • Multi-stage sampling involves selecting large primary sampling units, selecting smaller units within those larger units, and drawing samples from those smaller units.  
Also Read: Biases in Surveys: What You Need to Know 

What Does Non-Probability Sampling Entail? 

Non-probability sampling is one in which the population elements have no known probability of being chosen. Researchers make decisions based on their judgment, purpose, and convenience. A researcher builds a representative sample of the population by consciously choosing certain units of the universe.   

There is a high likelihood of non-probability sampling occurring when it is unclear how many people or items will be present. It is impossible to predict which sample component will make up the data set. Furthermore, this method frequently appears in qualitative research, where predicting population characteristics and behaviors is not the goal. There are several types of non-probability samples: 

  • Convenient sampling is one of the most popular non-probability sampling methods, in which sample units are picked based on convenience, such as being readily available for interviews.  
  • Quota sampling indicates that parameters are defined to ensure that the sample represents the desired characteristic. For this strategy to be effective, researchers need to understand the demographics of their target group.  
  • In snowball sampling, the researcher interviews one person and then asks him to nominate other people from the population for interviewing. A referral system helps select other respondents. 
  • Using judgmental sampling, the researcher selects a sample that he believes best represents the entire population. 
Also Read: 5 Simple Formulas for Success in Demographic Survey  

Key Distinction Between Probability And Non-Probability Sampling 

  1. Probability sampling is a method of sampling in which each unit of the population has an equal chance of being selected as a representative sample. In contrast, non-probability sampling involves the purposeful selection of specific population units to constitute a sample.  
  1. Random selection is the basis of probability sampling, while arbitrary selection is the basis of non-probability sampling.  
  1. Probability sampling is free of bias since it relies on randomization. However, non-probability sampling is biased because it depends on the researcher’s judgment regarding which elements to choose.  
  1. Probability sampling represents the entire population so that the results can be extrapolated to the whole population. Alternatively, non-probability sampling is better suited to those who wish to understand or create ideas because individuals have the freedom to choose.  
  1. Quantitative studies use probability sampling, while qualitative studies use non-probability sampling.  
  1. Probability sampling is typically used when a population has a finite number of elements, whereas non-probability sampling involves infinite factors.  
  1. Samples taken by probability serve to test hypotheses, whereas samples taken by non-probability serve to generate them.  

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Kultar Singh – Chief Executive Officer, Sambodhi

Kultar Singh
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