In research, sampling techniques are essential to the data collection process which has two different methods probability sampling and non-probability sampling. By randomly choosing people from a population, probability sampling ensures fairness and enables us to make accurate predictions about the entire group. However, non-probability sampling lacks unpredictability, which may introduce biases and restrict generalizability. For researchers to select the best approach based on the objectives and limitations of their study, an understanding of the difference between probability and non probability sampling is crucial.
In this article, we will talk about the difference between probability and non probability sampling to learn about the unique traits, benefits, and drawbacks of each strategy.
What is Sampling?
Sampling is the process of choosing a selected group or sample to represent the whole population. Probability sampling and non-probability sampling are the two main divisions of sampling techniques. Each participant has a fixed, known chance of being a part of the sample in the first scenario, whereas there is no such guarantee in the second scenario.
Although to the layperson these two ideas appear to be the same, in reality, they are distinct because in probability sampling each member of the population has an equal chance of being chosen, whereas this is not the case with non-probability sampling.
What is Probability Sampling?
Probability sampling is a sampling technique where each individual of the population has an equal chance of being included in the sample. Moreover, The randomization principle, on which this method is founded, ensures that every member of the population has an equal chance of being selected through the careful design of the system.
By employing this technique, researchers can draw statistical inferences, i.e., generalize the findings from the sample that was surveyed to the intended audience. The following list includes the probability sampling techniques:
- Simple Random Sampling
- Stratified Sampling
- Cluster Sampling
- Systematic Sampling
What is Non-Probability Sampling?
Non-probability sampling is the term used to describe a sampling technique in which not every person in the universe is given an equal chance to join the sample. As such, this method relies solely on the researcher’s subjective judgment for selection, with no probability associated with the population unit. As a result, it is not possible to extrapolate the sampler’s conclusions to the entire population. Here are some examples of non-probability sampling techniques:
- Convenience Sampling
- Quota Sampling
- Judgment or Purposive Sampling
- Snowball Sampling
Key Differences Between Probability and Non-Probability Sampling
Here are the key differences between probability and non-probability sampling that will help you understand both sampling techniques easily:
- Probability sampling is a sampling method in which every member of the population has an equal chance of being chosen as a representative sample whereas non-probability sampling is a sampling technique where the person who will be selected as a sample from the population is unknown.
- Probability sampling is also referred to as random sampling because it is based on chance or randomization whereas the randomization approach is not used in non-probability sampling for choosing a sample so it is regarded as non-random sampling.
- In probability sampling, the sampler randomly selects the representative to be a part of the sample, while in non-probability sampling, the researcher randomly selects the subject to be a part of the sample.
- In probability sampling, the odds of selection are fixed and known along with the selection probability being 0, as opposed to non-probability sampling, meaning that it is neither known nor stated.
- When doing a conclusive study, probability sampling is employed, however, nonprobability sampling should be employed while conducting exploratory research.
- While non-probability sampling produces somewhat biased outcomes, probability sampling produces results that are free from any biases.
- Probability sampling has a better level of population representation than nonprobability sampling since the participants are chosen at random by the researcher. Due to this, extrapolating results to the entire population is only feasible using probability sampling and not non-probability sampling.
- Nonprobability sampling creates the hypothesis, while probability sampling tests it.
Difference Between Probability And Non-Probability Sampling In Tabular Form
Here is the difference between probability and non probability sampling in tabular form for your easy understanding:
| Basis Of Difference | Probability Sampling | Non-probability Sampling |
| Selection Method | This method includes a random selection from the population. | This method includes non-random or purposeful selection from the population. |
| Equal Opportunity | In probability sampling, each member has an equal chance of selection. | In non probability sampling, there is an unequal chance of selection. |
| Representativeness | Probability sampling enhances the representativeness of the sample | Non-probability sampling may lead to biased or unrepresentativeness of the sample. |
| Statistical Influences | Probability sampling is reliable for making statistical influences. | Non-probability sampling has limited reliability for statistical influences. |
| Generalizability | Probability sampling allows for generalization to the population. | Non-probability sampling has Limited generalization to the population. |
| Complexity | Probability sampling can be complex to implement. | Non-probability sampling is generally simple to implement. |
| Types | Different types of probability sampling include Simple Random, Stratified, Systematic, and Cluster. | Different types of non-probability sampling include Convenience, Purposive, Snowball Quota, Judgemental, etc. |
| Examples | An example of probability sampling could be randomly selecting voters for a poll. | An example of non-probability sampling could be surveying friends and family for opinions. |
FAQs
What is sampling in research?
Ans. Sampling is the procedure used in research to choose a portion of people or things from a broader group to collect data and draw conclusions. Inferences about the entire population are to be drawn from the features of the selected sample, making data gathering and analysis more possible and effective.
What is non-probability sampling?
Ans. Non-probability sampling is a research technique when participants are not randomly chosen from a population. It lacks a known, equal likelihood of selection, which could produce biased outcomes. Convenience, purposeful, and snowball sampling are some examples of these. Non-probability sampling may restrict generalizability and statistical validity, although being advantageous in some situations.
What are the key differences between probability and non-probability sampling?
Ans. The key difference between probability and non probability sampling is that Random selection from a population is used in probability sampling to ensure that each person has an equal chance of being chosen which improves representativeness and generalizability. While, Non-probability sampling lacks randomness, which could cause bias and lessen generalizability.
What are the common types of probability sampling methods?
Ans. Common probability sampling techniques include simple random sampling where each member has an equal chance, stratified sampling where the population is divided into subgroups, systematic sampling where every nth member is selected, and cluster sampling where random groups are selected.
What is simple random sampling?
Ans. Simple random sampling is a probability-based research technique in which there is an equal and independent chance that each person in the population will be chosen for the sample. It reduces bias and ensures representativeness, enhancing the accuracy of statistical conclusions made from the data gathered.
The Final Words
So overall, the main difference between probability and non probability sampling is that the first one is based on the principle of randomization where every entity gets a fair chance to be a part of the sample, however, the second one relies on the assumption that the characteristics are evenly distributed within the population and gives the sampler the impression that whatever sample they choose will accurately reflect the entire population and yield accurate results.
We hope that you found this article informative and that all your queries about the difference between probability and non probability sampling are now cleared.
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