Understanding Survivorship Bias
Survivorship bias is a cognitive bias where individuals focus on successful outcomes while ignoring unsuccessful ones. This bias can lead to erroneous conclusions due to incomplete data, particularly from those who have failed or been eliminated from the group.
One of the most notable examples of survivorship bias is from World War II. The statistician Abraham Wald helped the Allies reduce bomber losses. Initially, recommendations focused on reinforcing areas of bombers that showed the most damage upon return from missions. Wald realized these suggestions were based only on surviving bombers. He proposed reinforcing the areas with little to no damage on surviving planes, reasoning that hits in these areas likely caused planes to be lost.
A Silent Illusion to Recognise and Correct
Survivorship bias is a subtle but widespread cognitive error that can distort the interpretation of data and lead to overly optimistic conclusions. In business and finance, focusing only on success stories – such as surviving companies or performing mutual funds – leads to ignoring valuable lessons from failures, aborted projects or individuals who did not respond to treatment. This leads to overestimating the effectiveness of certain strategies and underestimating the real risks.
To mitigate this effect, it is crucial to include less visible cases in the analyses: failed companies, closed funds, unsuccessful experiments. Only by working with representative samples of the entire population, and not just the successful ones, is it possible to draw valid conclusions and build more realistic and robust strategies. Awareness of survivor bias and active engagement in counteracting it is an essential step in improving the quality of decisions and analyses in any field.
