Benefits and drawbacks of Non-Probability vs. Probability Sampling for Research
The following is a brief introduction to the topic:
The importance of sampling in research is to ensure that the findings are reliable and accurate. In research, two major sampling techniques exist – probability and non-probability sampling. The probability sampling method is used to select individuals or elements from the target population. Non-probability sampling, on the other hand, is where the sampling units are selected based on the researcher’s judgment, convenience, and/or availability. The paper examines the pros and cons of non-probability vs. probability sampling.
Benefits of Probability Sample Sampling
- Reprographic of the Population
By using probability sampling, you can ensure that your sample will be representative of the study population. It is much more likely, by selecting random elements of the population to represent the entire population being studied, that the selected sample will reflect the whole population. The findings are more generalizable to the whole population.
- There is less bias
The probability sampling technique has a reduced bias potential because the samples are randomly selected from the study population. The results are more reliable because the researchers’ bias will be less evident in selecting the samples.
- Statistics inference
The statistical inference that is possible with probability sampling allows for estimation of population parameters based on sample data. Researchers can then make broad generalizations based on sample data.
- Allows for hypothesis testing
Hypothese testing is possible with probability sampling. This allows the researchers to compare differences observed between groups with the differences expected based upon probability. The observed differences can be compared to the expected ones based on probability.
- Results that are more accurate
As they use statistical methods to analyse the data, probability sampling helps in getting more accurate outcomes. The sample is likely to represent the entire population. This ensures more accurate results.
Advantages and disadvantages of probability sampling
The time required to randomly select a sample can make probability sampling methods time-consuming. It can take a long time to randomly select a population sample, particularly when there is a large population.
- Needs to be sampled
A sampling frame is required for probability sampling. This list contains all elements of the population being studied. If the sampling frame used is incorrect or incomplete, then the selected sample may not represent the entire population.
It can be expensive to use probability sampling methods. It is expensive to implement these techniques because the researcher must have an exhaustive list of all the people under study.
- Difficulties when obtaining representative samples
When obtaining a sample representative of the entire population is impossible, it can be challenging to apply probability sampling techniques. The population being studied may be too large, or spread out to make it difficult to compile a list that includes all of the constituents.
- Rare populations not suitable
Probability sampling might not work for rare populations. Probability sampling is not suitable for rare populations because it has low chances of producing a representative sample.
Benefits of non-probability sampling
They are also less time-consuming, as the researcher does not have to select random elements in the population. They are therefore more appropriate when there is a limited amount of time.
- Buy Cheaper
As they don’t require an exhaustive list of all the people being studied, non-probability methods can be implemented at a lower cost. The researchers are able to obtain a population sample without incurring large expenses.
- Ideal for populations with rare species
For rare populations, non-probability techniques work better. They are more suitable for rare populations because they enable the researcher to get a representative sample without having to rely on