Final answer:
Nonprobability sampling, like convenience sampling, does not give each population member an equal selection chance, while probability sampling does. Nonprobability sampling is suited to scenarios with limited resources or a need for quick insights, and probability sampling is used for accurate, representative results.
Step-by-step explanation:
The main difference between nonprobability sampling and probability sampling lies in the methods of selection. In nonprobability sampling, not all members of the population get an equal chance of being selected.
This includes methods like convenience sampling, where the sample is taken from a group that is easy to access. The cluster sampling is somehow a mix of both, where clusters are formed based on shared characteristics, and then a sample is taken at random from these clusters.
Probability sampling methods, on the other hand, give each member an equal chance of being chosen. This category includes simple random sampling, stratified sampling, systematic sampling, and cluster sampling when it is done in such a way that it represents the entire population evenly.
The appropriate use of these methods can vary. Nonprobability sampling is beneficial when resources or time are limited, such as polling a class for quick insights. Probability sampling is more appropriate when the goal is to get a representative sample that reflects the population, such as in formal research studies or public opinion polling.