What is Random Sampling?
One of the best ways to achieve unbiased results in a study is through random sampling. Random sampling includes choosing subjects from a population through unpredictable means. In its simplest form, subjects all have an equal chance of being selected out of the population being researched.
Methods
In random sampling, three methods are most common when conducting surveys. Random number tables, more recently known as random number generators, tell researchers to select subjects at an interval generated randomly. Mathematical algorithms for pseudorandom number generators may also be used. Another method used is physical randomization devices, which could be as simple as a deck of playing cards or an electronic device called ERNIE.
Benefits
One of the biggest benefits of using random sampling in a survey is the fact that, since subjects are obviously randomized, it is the best way to ensure that results are unbiased. It is also much faster and often less expensive to use random sampling and as a result is a much more efficient way to obtain results. Additionally, random sampling consistently provides results that are valid, making it easy for researchers to draw conclusions about large populations.
Risks
As with any survey, there is no way to guarantee that the results that come from a sample in a random survey are 100% accurate, although the results do tend to be more accurate than those obtained through other methods. The sample may not be representative of the larger population, which can incur a sampling error, but the chance of this occurring can be determined early in the survey by mathematical theories. Despite the problems associated with this method, it’s important to remember that every survey comes with measures of uncertainty.
When to Use Random Sampling
When surveying a large population it may not make sense to survey everyone in the population, as this would be very time consuming and often quite expensive. Random sampling in this case would be proportionate to the size of the population, and the results from surveying the samples would be later used to infer how the population as a whole may have responded and to draw conclusions about the larger group.
Interpreting Data
Once the random sampling survey has been conducted, the next step is to interpret the data received from the selected group. It’s necessary to organize the information that has been gathered before analyzing the data. It’s important to determine the confidence and error levels in the survey as well to make sure the data is as accurate as possible. You may interpret that data as following a certain distribution, known as Gaussian distribution, by ranking and comparing results.
Random sampling is a quick and easy way to obtain unbiased results about a population being surveyed. Because many other methods of surveying can come with a huge risk of bias, random sampling is often a top choice when designing surveys. Despite the margin of error that comes with any survey, random sampling is the best way to get the most accurate information.



