Random sampling allows researchers to prevent bias in their work and makes research on large populations more practical, explains the Royal Geographical Society. Random sampling reduces the number of subjects that a researcher needs to find results from the population.
When researchers conduct random sampling, they make a representation of the population under study, clarifies Pell Institute. A population, the entire unit of study, is often too large to research as a whole. Drawing a random sample provides a manageable way to make observations or hold an experiment. The units in the study represent the characteristics of the larger population. In probability sampling, every unit must have the same opportunity to take part in the study for simple random sampling. Researchers use a method such as a lottery system to insure an equal chance for each participant. This method allows the researchers to remain objective and increases external validity, the application of research results to a general population.
The University of Hawaii explains different types of sampling, including simple random sampling, systematic sampling, cluster sampling and others. The sampling technique depends on the type of data and research limitations. A larger random sample of the population decreases the sampling error, which increases the reliability of the results.