Survivorship bias is a cognitive bias that occurs when an analysis only considers the 'survivors' or successful entities of a group while overlooking the failures, thereby skewing the results and leading to erroneous conclusions. This bias stems from the human tendency to draw inferences based on incomplete data, largely due to the absence of information about non-survivors.
Survivorship bias works by filtering out non-survivors or failures and focusing only on successful cases, often leading to misleading conclusions. This results in a distorted view because the data analyzed represents only those who 'survived' or succeeded, rather than a complete picture that includes both successes and failures.
Survivorship bias can lead to overly optimistic expectations and underestimation of risks. By focusing only on successful outcomes or surviving entities, important lessons from failures are often disregarded, which can result in flawed strategies or the misallocation of resources.
To counteract survivorship bias, it is essential to seek out data on both success and failure cases, ensuring a comprehensive analysis. Including diverse perspectives and fostering an awareness of omitted data can help mitigate the effects of this bias.
One critique of survivorship bias is that it oversimplifies complex systems by ignoring a multitude of factors leading to success or failure. Critics argue that while acknowledging survivorship bias is beneficial, real-world scenarios often involve variables that are not easily accounted for.
Historical patterns of aircraft survivorship: An application of survivorship bias
Piatkowski, T. M., & Krug, E. J. (2019)
Journal of Historical Economics and Econometric History
Does the stock market overreact? The Journal of Finance, 40(3), 793-805
Bondt, W. F. M., & Thaler, R. (1985)
Performance persistence
Brown, S. J., & Goetzmann, W. N. (1995)
The Journal of Finance, 50(2), 679-698