Give one example in which Random Sampling is a better tool than Non-Random Sampling and vice versa.
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Explanation:
RANDOM SAMPLING
We’re dealing with random sampling whenever the following conditions are met:
(1) Every element in our population has a nonzero probability of being selected as part of the sample.
(2) We have accurate knowledge of this probability, known as the inclusion probability, for each element in the sampling frame.
If both of these criteria are met, it is possible to obtain unbiased results about the population from studying the sample. To obtain unbiased results, it may sometimes be necessary to use weighting methods; such weighting is possible precisely because we know each individual's probability of being included in the sample. Samples obtained under these conditions are also known as random samples.
The above definition leads us to conclude that we can only create a random sample if we have a sampling frame. A national census, a database of mailing addresses within a city and a list of a business’s customers are all examples of sampling frames that make random sampling possible. In each of the above cases, the population to be studied is different: the residents of a country, the households in a city and a business’s customers, respectively.
Once we have our sampling frame, the random sampling method defines the exact method we will use to select our sample; for example, simple random sampling, systematic sampling, stratified sampling, disproportional stratified sampling, cluster sampling, and so on.
NON-RANDOM SAMPLING
All that said, it’s not easy to meet the criteria imposed by random sampling:
(1) It is relatively unusual to have a sampling frame available to you when you’re conducting market studies.
(2) Ensuring that every individual in a population has a nonzero probability of being selected is just as difficult to accomplish; knowing every sampling unit’s exact inclusion probability is even more difficult. The individuals that cannot be selected as part of a sample are generally referred to as excluded units.
For these reasons—and to minimize costs—researchers often turn to other sampling methods, known as nonrandom sampling. When using these alternative methods, researchers generally select elements for the sample based on hypotheses about the population of interest, known as selection criteria. For example, if we’re selecting our sample by stopping people on the street, attempting to stop an equal number of men and women (to coincide with the presumed gender distribution in the population) would be a criterion of nonrandom sampling.
In these cases, since the selection of units for the sample isn’t random, we shouldn’t talk about error estimates. In other words, a nonrandom sample tells us about a population, but we don’t know how precisely: we can’t determine a margin of error or a confidence level.
These types of sampling methods include availability sampling, sequential sampling, quota sampling, discretionary sampling and snowball sampling.
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