# What is the difference between probability and non-probability sampling?

Quantitative Methodology

If you are conducting an original study for your dissertation research, you will need to choose a method of sampling to obtain your participants. Choosing an appropriate sampling method is important for both quantitative and qualitative studies. There are two general types of sampling methods: probability sampling and non-probability sampling. In this blog, we will explain these two types of sampling and when it is appropriate to use them.

The first type of sampling is probability sampling, which will always involve some sort of “random” or “probabilistic” process to select participants. The various forms of random sampling (including simple random sampling and stratified random sampling) are probability sampling techniques. In the most basic form of probability sampling (i.e., a simple random sample), every member of the population has an equal chance of being selected into the study. The participants selected for the study would be determined through some kind of “random” process, such as assigning a number to every member of the population and picking the numbers out of a hat, or using a table of randomly generated numbers. More complex forms (such as stratified random sampling) involve randomly selecting individuals from subgroups of the population to ensure those groups are appropriately represented in the study sample.

In any case, the goal is to obtain a sample that is representative of the population of interest, so that the results of the study may be generalized to the population. This makes probability sampling an ideal choice for quantitative studies in which the goal is to use statistical analysis to draw conclusions about the population. It may be less appropriate for qualitative studies in which the goal is to describe a very specific group of people and generalizing the results to a larger population is not the focus of the study.

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Non-probability sampling, on the other hand, does not involve “random” processes for selecting participants. In non-probability sampling, the members of the population will not have an equal chance of being selected, and in many cases, there will be members of the population who have no chance of being selected. For example, if your population of interest is college professors but you only invite professors from your school to participate, this would be a non-probability sample because professors from other colleges have no chance to participate. This method of convenience sampling, which involves selecting only participants who are readily accessible, is one of the most common. Keep in mind that any procedure that does not involve random selection from the population by the researcher, or involves self-selection of participants, would be considered a non-probability sampling method. Even the use of online survey hosting services (such as SurveyMonkey or Qualtrics) may be considered non-probability sampling, as their participant pools may not include every member of the population, or they may not use random processes to select participants for you.

Non-probability sampling is not ideal for quantitative research because results from these samples cannot be generalized to the larger population as confidently compared to probability samples. However, non-probability sampling is often used in quantitative research because probability sampling is not always feasible. Going back to the college professor example, it may not be possible for you to select a random sample from all possible college professors in the general population. You likely would not be able to compile a list of every single college professor in the population with their contact information. In these cases, quantitative researchers may resort to convenience sampling. On the other hand, non-probability sampling is well-suited for many types of qualitative research. This is because qualitative research is not always concerned with generalizing the results to a larger population. Qualitative researchers often use purposive sampling, a non-probability sampling technique in which the researcher chooses participants because they have specific expertise or insight regarding the phenomenon of interest.

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