Freedom from human bias and classification error remains one of the biggest advantages simple random sampling offers, as it gives each member of a population a fair chance of being selected. If done right, simple random sampling results in a sample highly representative of the population of interest. In theory, if a researcher has access to all the necessary data about a given population, only bad luck can compromise his sample’s representativeness.
Other sampling methods require much in-depth research and advance knowledge of a population prior to the selection of subjects. In simple random sampling, only the complete listing of the elements in a population (known as the sampling frame) is needed. A simple random sample, being highly representative of a population, also simplifies data interpretation and analysis of results. Trends within the sample act as excellent indicators of trends in the overall population. Many consider generalizations derived from a well-assembled simple random sample to have sufficient external validity.
While the randomness of the selection process ensures the unbiased choice of subjects, it could also, by chance, lead to the assembly of a sample which does not represent the population well. This random variation, independent of all human bias and in many cases difficult to pinpoint, is known as “sampling error.” The probability of incurring errors in sampling increases with decreased sample size. Researchers therefore set a sample size big enough to minimize the likelihood of freak results.
Although simple random sampling seems straightforward, to do it right you have to be certain you aren't mistakenly including bias in your sample. For example, if you decide you'll call people at home between 7 and 9 PM to ask them about their favorite television show, you're automatically eliminating everybody who's at work between 7 and 9 PM. You're also eliminating all those folks who are watching their favorite shows between 7 and 9 PM. To avoid those kinds of errors you need to take the time to understand the group you're trying to study, and make certain the way that you're randomly selecting does not include any hidden bias. That takes a lot of time.