What are Probability and Non Probability samples? copiously explain their application in Communication Research
In research, a sample is a subset of a population that is selected for study. Probability sampling is a method of sampling in which every member of the population has a known, non-zero chance of being selected for the sample. This means that the sample is representative of the population and statistical inferences can be made about the population based on the sample. Non-probability sampling is a method of sampling in which the selection of the sample is not based on the principles of probability theory. This means that the sample may not be representative of the population, and statistical inferences cannot be made about the population based on the sample.
Probability sampling is often used in communication research because it allows researchers to make more accurate inferences about the population being studied. For example, if a researcher wants to study the attitudes of a particular group of people towards a particular issue, they might use a probability sample to select a representative group of people from the population being studied. This would allow the researcher to make inferences about the attitudes of the entire population based on the attitudes of the sample.
Non-probability sampling is often used in communication research when it is not possible or practical to use probability sampling. For example, if a researcher wants to study the attitudes of a group of people who are difficult to access, such as celebrities or politicians, they might use a non-probability sample to select a group of people to study. This would allow the researcher to study the attitudes of this group, even though they are not representative of the population as a whole.
Both probability and non-probability sampling have their strengths and limitations, and the choice of which method to use depends on the research question being studied and the resources available to the researcher. It is important for researchers to consider the limitations of their sample when interpreting and generalizing their results.
