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Survivorship bias is a common cognitive error that skews one's perception of reality. It occurs when people focus only on the successful outcomes or survivors of a particular process or event while ignoring those who failed or were eliminated along the way. This bias can lead to faulty conclusions and poor decision-making because it overlooks important information that could provide a more accurate understanding of the situation.

The term "survivorship bias" originated during World War II when statisticians analyzed bullet hole patterns on returning aircraft. Initially, the military planned to reinforce areas with the most damage, assuming those were the most vulnerable spots. However, statistician Abraham Wald pointed out the flaw in this logic. By focusing only on the surviving planes, they were ignoring the ones that did not return -- those likely hit in critical areas. Thus, reinforcing the areas with the least damage on the returning planes was crucial for increasing survivability.

Impact on decision-making

Survivorship bias can be observed in various contexts, from financial markets to self-help advice. For instance, when studying successful entrepreneurs, one might only look at those who achieved great wealth and fame, overlooking the countless others who failed in their ventures. Similarly, in historical accounts, people often hear about the victorious armies or celebrated leaders, while the stories of defeated armies or less successful leaders are relegated to obscurity.

When investing, people may overestimate their chances of success by emulating the strategies of successful investors without considering the multitude who failed using the same approach. When it comes to education or career choices, people can create unrealistic expectations by presenting a skewed view of the path to success, causing one to overlook potential risks and challenges.

To mitigate survivorship bias, one must actively seek out and consider information from all outcomes, not just the successes. This involves conducting thorough research, analyzing successes and failures and being mindful of the inherent biases in the data.