Simple random sampling statistically measures a subset of individuals selected from a larger group or population to approximate a response from the entire group. Unlike other forms of surveying techniques, simple random sampling is an unbiased approach to garner the responses from a large group. Because individuals who make up the subset are chosen at random, each individual in the large population set has the same probability of being selected. This creates, in most cases, a balanced subset that carries the greatest potential for representing the larger group as a whole.

Although there are distinct advantages to using a simple random sample in research, it has inherent drawbacks. These disadvantages include the time needed to gather the full list of a specific population, the capital necessary to retrieve and contact that list, and the bias that could occur when the sample set is not large enough to adequately represent the full population.

The Time and Costs of Simple Random Sampling

In simple random sampling, an accurate statistical measure of a large population can only be obtained when a full list of the entire population to be studied is available. In some instances, details on a population of students at a university or a group of employees at a specific company are accessible through the organization that connects each population. However, gaining access to the full list can present challenges. Some universities or colleges are not willing to provide a full list of students or faculty for research. Similarly, specific companies may not be willing or able to hand over information about employee groups due to privacy policies.

When a full list of a larger population is not available, individuals attempting to produce simple random sampling must gather information from other sources. If publicly available, smaller subset lists can be used to recreate a full list of a larger population, but this strategy takes time to complete. Organizations that keep data on students, employees and individual consumers often impose lengthy retrieval processes that can stall a person's ability to obtain the most accurate information on the entire population set. (For related reading, see: What are the best selection methods for creating a simple random sample?)

In addition to the time it takes to gather information from various sources, the process may cost a company or individual a substantial amount of capital. Retrieving a full list of a population or smaller subset lists from a third-party data provider may require payment each time population data is provided. If the sample is not large enough to represent the views of the entire population during the first round of simple random sampling, purchasing additional lists or databases to avoid a sampling error can be prohibitive.

Bias in Random Sampling

Although simple random sampling is intended to be an unbiased approach to surveying, sample selection bias can occur. When a sample set of the larger population is not inclusive enough, representation of the full population is skewed and requires additional sampling techniques. To ensure a bias does not occur, researchers must acquire responses from an adequate number of respondents, which may not be possible due to time or budget constraints. (For related reading, see: When is it better to use systematic over simple random sampling?)