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Probability and Non-Probability Sampling

Probability and Non-Probability Sampling

A population is the total number of elements in a group while a sample is a portion of the population. Sample statistics—quantities such as sample mean that describe sample data—generalize the information about the population parameter. As such, we draw samples from a particular population mainly for two reasons. To begin with, when a population is large, it is expensive to study each member of the population. Besides, studying each member of a large population is time-consuming.

The following are two types of sampling methods: Probability sampling and non-probability sampling.

Probability Sampling

In probability sampling, every member of the population has an equal chance of being selected. Probability sampling techniques include simple random sampling, stratified random sampling, cluster sampling, and systematic sampling. These techniques will be discussed further in the next reading objective.

Non-Probability Sampling

In non-probability sampling, samples are selected on the basis of judgment or the convenience of accessing data. As such, non-probability sampling largely depends on a researcher’s sample selection skills. There are two types of non-probability sampling methods:

  1. Judgmental sampling: This type of sampling involves handpicking elements from a sample based on a researcher’s knowledge and expertise. It is important to point out that in this sampling method, the selection of samples is subjective. Obviously, such data could be skewed by the researcher’s bias and prejudice. Judgmental sampling consequently generates a sample that is not representative of the entire population.
  2. Convenience sampling: In this sampling method, a population element is selected based on how easily a researcher can access the element. Note that in this method, samples are selected conveniently, so they may not necessarily represent the whole population. This compromises the sampling accuracy. 

All else equal, probability sampling provides a more accurate and reliable representation of the population than non-probability sampling. A more convenient summary of sampling techniques is illustrated below:


A junior analyst wishes to study the spending patterns of employed investment professionals. To ease his data collection process, he selects only investment professionals in his firm. Which of the following is the most likely sampling method the analyst used?

  1. Cluster sampling.
  2. Convenience sampling.
  3. Judgmental sampling.


The correct answer is B.

In convenience sampling, a population element is selected based on how easily a researcher can access it. Clearly, the analyst used convenience sampling since he only focused on the investment professionals in his firm. Obviously, his choice was guided by the ease with which he could access the investment professionals in his firm. 

A is incorrect. Cluster sampling is a type of probability sampling, which cannot fit in this context.

C is incorrect. Judgmental sampling involves handpicking elements from a sample based on a researcher’s knowledge and expertise.

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