Judgement sampling is not a probability sampling technique
<h3>Probability Sampling</h3>
Probability sampling is a technique in which the researcher chooses samples from a larger population using a method based on probability theory. For a participant to be considered as a probability sample, he/she must be selected using a random selection.
The most critical requirement of probability sampling is that everyone in your population has a known and equal chance of getting selected.
1. Simple random sampling, as the name suggests, is an entirely random method of selecting the sample. This sampling method is as easy as assigning numbers to the individuals (sample) and then randomly choosing from those numbers through an automated process. Finally, the numbers that are chosen are the members that are included in the sample.
2. Stratified random sampling involves a method where the researcher divides a more extensive population into smaller groups that usually don’t overlap but represent the entire population. While sampling, organize these groups and then draw a sample from each group separately.
3. Random cluster sampling is a way to select participants randomly that are spread out geographically. For example, if you wanted to choose 100 participants from the entire population of the U.S., it is likely impossible to get a complete list of everyone. Instead, the researcher randomly selects areas (i.e., cities or counties) and randomly selects from within those boundaries.
From the above explanation, cluster sampling, simple random sampling and stratified random sampling are types of probability sampling except for judgement sampling.
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