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Simple random sample is a statistics term that describes the most basic, fundamental sample from a data population.Â  Â The simple random sample is intended to be an equivalent representation of the entire data population.Â  For instance, if the data population is 5,000 employees, then a random selection of 100 of those employees from the employee list will represent the entire employee population.Â  In the selection process, each of the 5,000 employees has an equal chance of ending up in the 100-employee sample.

Simple random sampling is generally used when there is not much information about a population, or the data is very diverse and not easily grouped into subgroups.Â  Another appropriate time to use simple random sampling is when the data is uniform except for one characteristic.Â  An example would be the quarterly stock dividend payout for a selected stock over a number of years.Â

If there are distinctive characteristics within a population, it is better to use more specific types of sampling, such as stratified random sampling.Â  This insures all subgroups within the population are represented in the sample.Â  In our example of selecting 100 employees from the population of 5,000, if half of the population is female and half is male, there is a chance that a 100-employee sample might consist of all men or all women.Â  A stratified random sample would consist of randomly selecting 50 women and 50 men from the list of 5,000 employees.

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