Representative Sample vs. Random Sample: An Overview
Representative sampling and random sampling are two techniques to help ensure that data is accurate and unbiased. 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.
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 abiased sample of a population (or non-human factors) in which all individuals, or instances, were not equally likely to have been selected.
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.
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 abiased 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.
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).
One of the largest studies using representative and random sampling techniques is the monthly Employment Situation Summary by the Bureau of Labor Statistics. It is based on a survey of 122,000 businesses and government agencies.
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 hom*ogeneous 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.
How Do You Know If a Sample Is Representative?
In statistics, a representative sample should be an accurate cross-section of the population being sampled. Although the features of the larger sample cannot always be determined with precision, you can determine if a sample is sufficiently representative by comparing it with the population. In economics studies, this might entail comparing the average ages or income levels of the sample with the known characteristics of the population at large.
What Is a Stratified Random Sample?
A stratified random sample is a statistical procedure that takes multiple random samplings from different subsets, or "strata", of the population. This is more complicated than a simple random sample but ensures that the final sample will be a representative cross-section of the population at large.
How Do Statisticians Reduce Errors?
Reducing sampling errors and selection bias are among the biggest challenges facing statistical researchers. Researchers must carefully assess their methodology to eliminate potential sources of unintended bias. Larger sample sizes and significance testing can also help reduce the size and impact of statistical errors.
The Bottom Line
Random samples and representative samples are two methods used by researchers to gain statistical insights about a set of data, without having to examine the entire population. Used together, these two methods can help ensure that statisticians are basing their data on an accurate subset of the population.
