Simple random sampling, the most basic among the probability sampling techniques, involves assembling a sample in such a way that each independent, same-size subset within a population is given an equal chance of becoming a subject. Methods for simple random sampling include lotteries and random number tables. In spite of its advantages, researchers limit the use of simple random sampling, especially when it comes to population-based surveys.

## Advantage - Representativeness and Freedom from Bias

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.

## Advantage - Ease of Sampling and Analysis

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 generalisation derived from a well-assembled simple random sample to have sufficient external validity.

## Disadvantage - Errors in Sampling

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 minimise the likelihood of freak results.

## Disadvantage - Time and Labor Requirement

As a complete and up-to-date frame is the minimum requirement for a good simple random sample, data gathering often entails a lot of time and labour, especially in cases involving large target populations. The trouble with obtaining a complete sampling frame stems from the inaccessibility of existing data or from the difficulty of constructing the frame on one's own. Comprehensive lists, if they do exist, are often not in the public domain. To gain access, the researcher must either pay for the data or apply for permissions -- a possibly lengthy and cumbersome procedure. These considerations greatly limit simple random sampling's applicability to most population studies.