Probability and Non-probability Sampling

There are many types of sampling techniques, but the most commonly used sampling techniques are probability and d-non-probability sampling. During a research study, it is impossible to use data from the whole population because it may be too hard to analyze. As a result, the researcher uses sampling techniques in order to avoid biases (Langer, 2018). Basically, Sampling is a type of statistical analysis in which a set number of observations are taken from a larger group or population. There are many factors that are considered when choosing a sampling technique, but researchers usually consider the method of analysis to be used.

In Sampling, researchers usually select individuals randomly or non-randomly. This is what brings the main difference between probability sampling and non-probability Sampling. In a non-probability sample, people are chosen based on factors other than chance. That means there are no random criteria used during choosing. In contrast, Random selection is used in probability sampling, which lets you draw strong statistical conclusions about the whole group. The above difference tells us that in non-probability sampling, there is no equal chance of being selected, whereas in probability sampling, people have an equal opportunity of being selected (Langer, 2018). Thus non-probability Sampling involves the researcher selecting subjects at random, whereas probability sampling involves selecting representatives arbitrarily. Another difference between probability sampling and non-probability Sampling is brought by the opportunity of selection. With probability sampling, you’ll always know your odds or the chance of being selected. However, with non-probability, the likelihood of being selected is either zero or not known at all. A research study is carried out in order to explain or conclude something. Therefore, in this case, when a definitive answer is sought, probabilistic Sampling is used, whereas nonprobability Sampling is appropriate for exploratory studies. One of the advantages of random or probability Sampling is that the results are unbiased because everyone in the population has a fair of being selected. However, in non-probability sampling, the results are biased because the method does not give an equal chance of selection. When participants in probability sampling are chosen at random by the researcher, it is more representative of the population as a whole than nonprobability Sampling (Langer, 2018). This is why probability sampling allows for the findings to be extrapolated to the full population, but non-probability Sampling does not. Lastly, while probability sampling is useful for verifying hypotheses, nonprobability Sampling actually creates them.

Researchers would use conditional probability instead of non-conditional probability because it helps them understand the connection between occurrences (Buelens et al., 2018). This is because the occurrence of one event depends on the occurrence of the other event. However, with non-conditional probability, the researcher is unable to understand the relationship between occurrences because the occurrences are independent. 

References

Langer, G. (2018). Probability versus non-probability methods. The Palgrave handbook of survey research, 351-362.

Buelens, B., Burger, J., & van den Brakel, J. A. (2018). Comparing inference methods for non?probability samples. International Statistical Review, 86(2), 322-343.