Why do we misjudge groups by only looking at specific group members?

The 

Survivorship Bias

, explained.
Bias

What is the Survivorship Bias?

Survivorship bias is a cognitive shortcut that occurs when a successful subgroup is mistaken as the entire group, due to the invisibility of the failure subgroup. The bias’ name comes from the error an individual makes when a data set only considers the “surviving” observations, excluding points that didn’t survive.1

An illustration depicting survivorship bias, with a stick figure standing at the peak of a red triangle, representing the 0.1% who succeeded, while the base of the triangle, representing the 99.9% who failed, is much larger. The stick figure says, 'If I can do it, anyone can!' highlighting the bias of focusing only on the successful outcomes.

Where this bias occurs

Examples of survivorship bias are noticeable in a wide range of fields, particularly in the corporate world. Students in business school can recall how “unicorn start-ups” are commonly applauded within the classroom, serving as an example of what students should strive for — an archetypal symbol of success. Even though Forbes reported that 90% of start-ups fail, entire degrees are dedicated to entrepreneurship, with dozens of students claiming that they will one day find a start-up and become successful.2

By looking at successful start-up founders, like Steve Jobs, Bill Gates, and Mark Zuckerberg, an individual could conclude that to reach their level of success, they must simply have an idea, drop out of school, and dedicate time to their big idea.

In Scientific American, Professor Michael Shermer and Larry Smith from the University of Waterloo describe how advice about commercial successes distorts individual perceptions, as we tend to ignore college dropouts who don’t become successful entrepreneurs or businesses that have failed.3

Simply put, many forget that these unicorn start-ups are just that: unicorns. Of the thousands of people who attempt to follow the same paths as these business tycoons, most fail. Still, their stories of failure aren’t shared as widely as success stories, giving others an inflated idea of our capabilities and potential achievements. That is not to say that hard work and talent will not lead to success, but rather that as a society, we tend to ignore common failures and hold onto success stories as proof of what is possible. Instead, in this hypothetical, we must also consider that things like luck, timing, connections, and socioeconomic background have played a part in well-known founders’ achievements.

Sources

  1. Survivorship Bias - Overview, Impact, and How to Prevent. (2020, May 15). Retrieved from https://corporatefinanceinstitute.com/resources/knowledge/other/survivorship-bias/
  2. Patel, N. (2015, September 02). 90% Of Startups Fail: Here's What You Need To Know About The 10%. Retrieved July 27, 2020, from https://www.forbes.com/sites/neilpatel/2015/01/16/90-of-startups-will-fail-heres-what-you-need-to-know-about-the-10/
  3. Robert J Zimmer (2013-03-01). "The Myth of the Successful College Dropout: Why It Could Make Millions of Young Americans Poorer". The Atlantic.
  4. Donohue, W. (2019, September 24). 7 Lessons on Survivorship Bias that Will Help You Make Better Decisions. Retrieved July 27, 2020, from https://blog.idonethis.com/7-lessons-survivorship-bias-will-help-make-better-decisions/
  5. M., Han, L., MD, P., & Singhal, S. (2020, May 27). Major challenges remain in COVID-19 testing. Retrieved July 27, 2020, from https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/major-challenges-remain-in-covid-19-testing 
  6.  Survivorship Bias: The Tale of Forgotten Failures. (2019, December 02). Retrieved from https://fs.blog/2019/12/survivorship-bias
  7. Madhavan, A. (2020, June 03). Correlation vs Causation: Understand the Difference for Your Product. Retrieved from https://amplitude.com/blog/2017/01/19/causation-correlation
  8. Powell, I., Ingram, N., & Broughton, G. (2016, March 28). Survivorship bias - lessons from World War Two aircraft. Retrieved from https://clearthinking.co/survivorship-bias/
  9. Vanguard. (2015, March). What is ‘survivorship bias’ and why does it matter? [Press release]. Retrieved from  
  10. Hayes, A. (2020, June 03). Mutual Fund Definition. Retrieved from https://www.investopedia.com/terms/m/mutualfund.asp
  11. https://www.vanguard.co.uk/documents/adv/literature/survivorship-bias.pdf
  12. Elton; Gruber; Blake (1996). "Survivorship Bias and Mutual Fund Performance". Review of Financial Studies. 9 (4): 1097–1120. doi:10.1093/rfs/9.4.1097. In this paper the researchers eliminate survivorship bias by following the returns on all funds extant at the end of 1976. They show that other researchers have drawn spurious conclusions by failing to include the bias in regressions on fund performance.
  13. Thomas, J. (2019, April 23). Bullet Holes & Bias: The Story of Abraham Wald - mcdreeamie-musings. Retrieved from https://mcdreeamiemusings.com/blog/2019/4/1/survivorship-bias-how-lessons-from-world-war-two-affect-clinical-research-today

About the Authors

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Dan Pilat

Dan is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. Dan has a background in organizational decision making, with a BComm in Decision & Information Systems from McGill University. He has worked on enterprise-level behavioral architecture at TD Securities and BMO Capital Markets, where he advised management on the implementation of systems processing billions of dollars per week. Driven by an appetite for the latest in technology, Dan created a course on business intelligence and lectured at McGill University, and has applied behavioral science to topics such as augmented and virtual reality.

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Dr. Sekoul Krastev

Sekoul is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. A decision scientist with a PhD in Decision Neuroscience from McGill University, Sekoul's work has been featured in peer-reviewed journals and has been presented at conferences around the world. Sekoul previously advised management on innovation and engagement strategy at The Boston Consulting Group as well as on online media strategy at Google. He has a deep interest in the applications of behavioral science to new technology and has published on these topics in places such as the Huffington Post and Strategy & Business.

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