Nonprobability sampling designs ought to be preferred to probability sampling designsin some cases. Explain with an example.

Nonprobability sampling designs can be preferred over probability sampling designs in certain situations, particularly when it is more important to gather in-depth information from a specific group rather than generalizable data from a larger population.

For example, consider a scenario where a researcher is studying the effects of a new drug on patients with a rare disease. In this case, it would be impractical to use a probability sampling design, as the population of people with this rare disease is very small and spread out. It would be difficult, time-consuming, and expensive to randomly select a sample that is representative of all people with this disease.

Instead, the researcher could use a nonprobability sampling design, such as purposive sampling, to select a sample of patients who are readily available and willing to participate in the study. This would allow the researcher to gather detailed information about the effects of the drug on these specific patients, even though the results may not be generalizable to all people with the disease.

In this case, the nonprobability sampling design is preferred because it allows the researcher to conduct the study in a more practical and efficient manner, even though it may sacrifice some degree of representativeness.