What is a sample that fairly represents a population because each member has an equal chance of being included?

Representative Sample vs. Random Sample: An Overview

When conducting statistical analyses, economists and researchers seek to reduce sampling bias to a near negligible level. The danger of sampling bias is that it can result in a biased sample of a population (or non-human factors) in which all individuals, or instances, were not equally likely to have been selected.

Key Takeaways

• When conducting statistical analyses, economists and researchers seek to reduce sampling bias to a near negligible level.
• The danger of sampling bias is that it can result in a biased sample of a population (or non-human factors) in which all individuals, or instances, were not equally likely to have been selected.
• If sampling bias is not accounted for, the results of a study or an analysis can be wrongly attributed.
• Representative sampling and random sampling are two techniques used to help ensure data is free of bias.
• 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.

In order to reduce the likelihood of biased samples, statisticians and economists typically try to guarantee that three basic criteria are met in every sample analysis or study. This way, statisticians and economists can make more confident inferences about a general population from the results obtained.

• 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. In fact, they are often used in tandem to reduce the degree of sampling error in a study. When combined, these two methods allow for greater confidence in making statistical inferences from the sample in regard to the larger group.

Representative Sample

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 larger society under examination. Some examples include sex, age, education level, socioeconomic status (SES), or marital status. A larger sample size reduces the likelihood of sampling errors and increases the likelihood that the sample accurately reflects the target population.

Random Sample

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).

Special Considerations

For economists and statisticians collecting samples, it is imperative that they ensure that bias is minimized. If sampling bias is not accounted for, the results of a study or an analysis can be wrongly attributed. 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.

One of the most effective of these techniques is known as stratification. With stratification, the larger population is broken down into subgroups—or strata—of a fairly homogeneous nature. Then, an equal number of group members is selected from each stratum.

Another common method of achieving a random or representative sample is referred to as systematic sampling. With this method, to begin, members—or elements—of a study, are chosen from a random starting point. Then, selection proceeds at fixed, periodic intervals.

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