AI Summary & Key Takeaways
AI InsightHowever, population characteristics cannot be directly or indirectly estimated by using non-random sampling techniques.
📌 3 Key Takeaways
Abstract
The most commonly used sampling techniques in systematic investigations are probability and nonprobability methods. While probability sampling is based on the principle of a random selection of participants in a particular study, non-random selection is the basis of probability sampling. The random and non-random classifications appear to have some potential flaws and are insufficient to represent all sampling procedures involving human participants. Similarly, most authors believe that they use random sampling techniques, although, in reality, they do not use true random sampling. Therefore, the objective of this article is to highlight that sampling techniques can be characterized as true-random, quasi-random, or nonrandom, rather than merely random and non-random. Attempts have been made to show how inadequate random and non-random sampling methods are, the characteristics of true-random, quasi-random, and nonrandom sampling procedures, and when each sampling procedure is appropriate. Since each unit of the population is randomly selected and the chance of selecting the unit is equal, a real random sample is used to estimate the characteristics of the population directly from the sample. With quasi-random sampling, it is not possible to directly estimate population characteristics, but only indirectly. However, population characteristics cannot be directly or indirectly estimated by using non-random sampling techniques.