Understanding that focusing only on successes obscures the failures that inform realistic probability assessments.
Understanding that focusing only on successes obscures the failures that inform realistic probability assessments.
Survivorship bias occurs when we concentrate on people or things that made it past some selection process and overlook those that did not, typically because of their lack of visibility. By focusing exclusively on the survivors of a particular endeavor or situation, we systematically overestimate the probability of success and misunderstand the true characteristics of the population.
This bias leads to flawed conclusions because the failed cases—which are often equally instructive or more instructive—are missing from our analysis. The classic example involves WWII aircraft armor studies, where engineers wanted to reinforce planes based on returning aircraft, but the missing planes held the crucial information about where they were actually being hit.
The concept gained recognition through Abraham Wald's work during World War II at the Statistical Research Group. When the US Army Air Forces asked where to add armor to bombers, Wald pointed out that the data they had—bullet holes in returning planes—represented places planes could be hit and survive.
The holes in returning planes indicated where armor was NOT needed because those hits didn't bring the plane down. The missing data—planes that didn't return—showed where hits were fatal. This insight saved countless lives. The term "survivorship bias" emerged later as psychologists and statisticians studied errors in reasoning about past successes.
Startup Success Advice: Successful entrepreneurs often give advice about what made them successful. But survivorship bias means we don't hear from the vast majority who followed the same advice and failed. The advice may be wrong, incomplete, or only relevant under specific conditions that can't be replicated.