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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.

Expert Solution Preview

Introduction:
When conducting research studies, it is essential to use sampling techniques to collect data from a smaller group of individuals that can be analyzed and used to make conclusions about the entire population. The two main types of sampling techniques are probability and non-probability sampling. This response will discuss the differences between these two types of sampling techniques.

1. What is the main difference between probability and non-probability sampling?
Probability sampling involves selecting representatives arbitrarily, while non-probability sampling involves the researcher selecting subjects at random or based on factors other than chance. In probability sampling, individuals have an equal opportunity of being selected, whereas in non-probability sampling, there is no equal chance of being selected.

2. What type of research studies is probability Sampling appropriate for?
Probability sampling is appropriate for research studies that seek a definitive answer as it allows for the findings to be extrapolated to the full population. It is useful for verifying hypotheses.

3. What type of research studies is non-probability Sampling appropriate for?
Non-probability Sampling is appropriate for exploratory studies as it does not give an equal chance of selection. It actually creates hypotheses.

4. Why are the results of probability sampling considered to be unbiased?
The results of probability Sampling are considered to be unbiased because everyone in the population has an equal chance of being selected. This allows for more representative findings of the population as a whole.

5. Why would researchers use conditional probability instead of non-conditional probability?
Researchers would use conditional probability instead of non-conditional probability because it helps them understand the connection between occurrences. This is because the occurrence of one event depends on the occurrence of the other event. Non-conditional probability assumes that the occurrences are independent and does not allow for this understanding.

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