What it is
Survivorship bias is a type of sample selection bias that occurs when an individual mistakes a visible successful subgroup as the entire group. In other words, survivorship bias occurs when an individual only considers the surviving observation without considering those data points that didn’t survive in the event.
Why survivorship bias happens
Survivorship bias occurs in many disciplines, professions, and fields of research. Survivorship bias can be attributed to the fundamental misunderstanding of cause and effect, and a misunderstanding of correlation versus causation.
Example 1 – Financial systems
The Survivorship Bias occurs in our financial systems, when individuals calculate performance results of groups of investments, such as mutual funds, using only the surviving data at the end of the period, excluding those funds or companies that no longer exist. Typically, mutual funds no longer exist due to poor performance, so omitting them from performance studies usually skews data in an overly positive light.
Example 2 – Medical research
The Survivorship Bias can also be observed in its impact on medical research. In 2010, the Harvard Medical School and Beth Israel Deaconess Medical Center (BIDMC), attempted to conduct a trial on trauma patients and to better their survival outcomes from different types of medical procedures. Due to the specificity of the trial outcomes and the assumption that more patients would survive than was accurate, the trial was only able to recruit 573 patients, of their 1,502 total patients. This study’s failure was due to survivorship bias, as the trial only included patients who had survived their initial injury, and who had then received care in the Energy Department before then receiving 4-8 blood transfusions.
How to avoid it
Once individuals learn about the Survivorship Bias, they can avoid the bias by considering what data points may be missing from a dataset and using accurate data sources that do not omit key observations.