What is Sampling Bias?
Sampling bias, also referred to as sample selection bias, refers to errors that occur in research studies when the researchers do not properly select their participants.
Ideally, people participating in a research study should be chosen randomly while still adhering to the criteria of the study. When researchers fail to select their participants at random, they run the risk of severely impacting the validity of their results and findings because their sample does not accurately reflect the population of interest.
Related: What is Sampling Error & How Can I Reduce It?
An Example of Sampling Bias
Sampling bias is far too common in research, and it can even be committed by the most experienced professionals.
It is so common, in fact, that one of the most powerful and famous examples of sampling bias being committed on a grand and impactful scale occurred during the Truman-Dewey United States presidential race of 1948.
During the race, a political telephone survey was conducted nationwide. The results of the survey implied that Dewey would win over Truman in a heavy-handed landslide; however, the study failed to account for the fact that telephones were still a fairly revolutionary and expensive form of technology.
Due to the cost of telephones in 1948, only a small number of wealthy families owned them and kept them in their homes. Therefore, the political telephone survey was only presented to participants that were part of relatively wealthy families, and at the time, wealthy families tended to support Dewey while lower-middle class to lower class families were more likely to support Truman.
By failing to consider the population of Americans that owned telephones in 1948, the researchers conducting the telephone survey committed sampling bias. As a result, they received severely skewed response data.
Instead of distributing a more effective survey to a sample that more accurately represented the population of the United States at the time, the researchers ended up with inaccurate and unrepresentative insights.
The conductors of the survey were confident that Dewey would win the presidential race with ease, while in the end, it was Truman that ended up becoming the leader of the free world.
How to Avoid Sampling Bias in Research
Use Simple Random Sampling
One of the most effective methods that can be used by researchers to avoid sampling bias is simple random sampling, in which samples are chosen strictly by chance. This provides equal odds for every member of the population to be chosen as a participant in the study at hand.
The great thing about simple random sampling is that no effort is required by your potential participants.
For example, a computer can be used to randomly select names from a master list, and the selected names can become participants in the study. Similarly, random selection can be performed on a graphic calculator by using the command “Rand.”
Related: The Methods of Probability Sampling
Use Stratified Random Sampling
Another method that can be used to avoid sampling bias is stratified random sampling.
Stratified random sampling allows researchers to examine the population that they will be working with in their study, and comprise an accurately representative sample accordingly.
For example, stratified random sampling is effective if there are 1,000 individuals in a population and 10 people from the population are required to conduct a study. If 500 members of the population are women, and 500 members of the population are men, then the researchers’ sample should accurately reflect this.
This means that the sample must be comprised of five women and five men.
Stratified random sampling enables the researchers to become aware of this information prior to building their sample, which allows them to avoid sampling bias.
Related: What is Stratified Sampling & When is it Used?
Conclusion
Sampling bias is far too common in research, and can even be committed by the most experienced professionals.
As such, it’s imperative to check and double check your methodology for creating accurately representative samples while considering the launch of a new research project.
Do you have a sampling bias horror story that you’d be willing to share? If so, we want to hear it! When did sampling bias end up skewing the results of one of your studies? Sound off in the comments!