Representative Sample vs. Random Sample: What's the Difference?

Economists and researchers seek to reduce sampling bias to near negligible levels when employing statistical analysis. Three basic characteristics in a sample reduce the chances of sampling bias and allow economists to make more confident inferences about a general population from the results obtained from the sample analysis or study:

• Such samples must be representative of the chosen population studied.
• They must be randomly chosen, meaning that each member of the larger population has an equal chance of being chosen.
• They must be large enough so as not to skew the results. The optimal size of the sample group depends on the precise degree of confidence required for making an inference.

Representative sampling and random sampling are two techniques used to help ensure data is free of bias. These sampling techniques are not mutually exclusive and, in fact, they are often used in tandem to reduce the degree of sampling error in an analysis and allow for greater confidence in making statistical inferences from the sample in regard to the larger group.

A representative sample is a group or set chosen from a larger statistical population or group of factors or instances that adequately replicates the larger group according to whatever characteristic or quality is under study.

A representative sample parallels key variables and characteristics of the large society under examination. Some examples include sex, age, education level, socioeconomic status (SES), or marital status. A larger sample size reduced sampling error and increases the likelihood that the sample accurately reflects the target population. 

A random sample is a group or set chosen from a larger population or group of factors of instances in a random manner that allows for each member of the larger group to have an equal chance of being chosen. A random sample is meant to be an unbiased representation of the larger population. It is considered a fair way to select a sample from a larger population since every member of the population has an equal chance of getting selected.

People collecting samples need to ensure that bias is minimized. Representative sampling is one of the key methods of achieving this because such samples replicate as closely as possible elements of the larger population under study. This alone, however, is not enough to make the sampling bias negligible. Combining the random sampling technique with the representative sampling method reduces bias further because no specific member of the representative population has a greater chance of selection into the sample than any other.

[Important: Effective random sampling can be accomplished through a variety of techniques that lead to more precise results and allows for greater confidence in making statistical inferences in regard to the chosen population.]

One of the most effective of these techniques is known as stratification, breaking the larger population into subgroups or strata of a fairly homogeneous nature and choosing an equal number of group members from each stratum. In other common methods such as systematic sampling, members are chosen to begin from a random starting point and proceeding at fixed periodic intervals.

• A representative sample is a group or set chosen from a larger statistical population according to specified characteristics.
• A random sample is a group or set chosen in a random manner from a larger population.
• The two can be used together to help reduce sample bias.