Simple Random Sample: Advantages and Disadvantages

Simple Random Sample: Advantages and Disadvantages

A simple random sample is used by researchers to statistically measure a subset of individuals selected from a larger group or population to approximate a response from the entire group. This research method has both benefits and drawbacks.

Simple Random Sample: An Overview

Unlike other forms of surveying techniques, simple random sampling is an unbiased approach to garner the responses from a large group. 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.

Advantages of a Simple Random Sample

Random sampling offers two primary advantages.

Lack of Bias

Because individuals who make up the subset of the larger group 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.


As its name implies, producing a simple random sample is much less complicated than other methods, such as stratified random sampling. As mentioned, individuals in the subset are selected randomly and there are no additional steps.

To ensure 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.

Disadvantages of a Simple Random Sample

The drawbacks of this research method include:

Difficulty Accessing Lists of the Full Population

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.

Key Takeaways

  • A simple random sample is one of the methods researchers use to choose a sample from a larger population.
  • Major advantages include its simplicity and lack of bias.
  • Among the disadvantages are difficulty gaining access to a list of a larger population, time, costs, and that bias can still occur under certain circumstances.

However, gaining access to the whole list can present challenges. Some universities or colleges are not willing to provide a complete 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.

Time Consuming

When a full list of a larger population is not available, individuals attempting to conduct 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 researcher's ability to obtain the most accurate information on the entire population set.


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 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.

Sample Selection Bias

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.