Selection bias is an error in scientific studies caused by biased recruitment or data analysis. Random sampling and retention methods can help avoid bias, as can proper statistical analysis and peer review. Researchers should plan ahead to address potential biases.
Selection bias is an error with the methodologies behind recruiting and retaining study participants or analyzing the obtained data, which makes the results less reliable. It’s one of several biases that can discolor a study if researchers don’t anticipate them and take steps to avoid them. In a good scientific study report, researchers can discuss all the methods used to allow readers to judge whether bias may have tainted the results.
An example of a selection bias is a sampling bias, where candidates for a study are not chosen randomly, which would tend to skew the data. A truly random sampling method draws in a broad assortment of people from the target population to avoid problems that might arise with a small sample, such as false correlations that are actually a result of who participated, rather than what is being studied. For example, recruiting for a pet health study that focuses on veterinary offices would create sampling bias, because people with healthy pets would not be recruited.
A selection bias can also come into play with retention. Over the course of a study, especially a long one, there tends to be a certain amount of attrition as people drop out or become ineligible for various reasons. If this rate is high, it can skew the final results by narrowing the sample and making it less random. If a study doesn’t have adequate measures to encourage participants to see it through to the end, it may have a selection bias problem.
Stopping a trial early can interfere with the time frame and create false or misleading data. Similarly, not properly controlling data and using poor statistical analysis methods can create selection bias. Researchers can also confuse cause and effect, create false correlations, or misinterpret study results. If they analyze the data in a way that confirms false conclusions, their end results may be less valuable.
A certain degree of bias can be difficult to avoid with scientific research. Before a project begins, researchers can sit down to discuss possible biases and ways to address them, so they can plan ahead to address issues such as selection biases. They monitor the study as it occurs for signs of emerging bias and are careful about how they evaluate and discuss the data. Peer review is an important part of this process, as it allows for input from third parties who are less likely to be interested in the results and therefore can be honest in their assessments of whether a project is worthwhile.
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