Success Story – Harmless Economics

Do you want success in the new year? Are you willing to change yourself into a better version? Are you open to learning from success stories?

Peters and Waterman in their book “In Search of Perfection” tell you 8 things in common with 15 of America’s most successful businesses. Collins’ “Good to Great” recounts the 7 characteristics of 11 “great” companies. TechCrunch (2013) summarizes the common characteristics of 39 billion-dollar startups (unicorns). The New York Times Magazine (2018) lists the morning routines of 300 successful people. Can we really “learn” from well-reported success stories? From a statistical perspective, when reading these stories, we need to note two sources of “selection bias”. First, stories that focus on success often miss the failure side. Traits common to successful people may also be common among unsuccessful people. Therefore, it is difficult to attribute a certain trait (e.g. wake up at 5am) from a small sample to the universal formula for success.

The selection of traits that lead to success is also a point worth noting. Each individual or company possesses many different characteristics, and an emotional story often focuses on one or a few characteristics. For each small group of successful people, it is easy to find some similarity between them. Or for each preselected feature, we can find a small successful sample to illustrate it. This means that the trait common among a select group of successful people may not even be a common denominator among successful people.

The figure below depicts these two sources of “selection bias”. Imagine a world with only 2 groups of people who succeed (S) and fail (F). Some of them have a particular personality, for example, are “circled” and others are not.

If we just stare at a small selection of successful people in this figure (smallest corner), it seems that success is related to the characteristic of being “squared” because out of 5 successful people, 4 are circled. . If we extend our view to a sample of all successful people (middle corner), we can change the conclusion because out of 17 successful people, only 6 are circled. If we zoom in on the whole picture, we see again that the squaring characteristic is actually more associated with the losers than the successful ones.

While selected stories may not really help to decipher the secret to success, they do have a huge impact on readers’ perceptions of life and influence decision-making. A team of scientists in the US and Canada recently conducted an experiment in which participants were asked to predict whether a startup would become a billion-dollar unicorn and get paid if they judge. guess right.

Before that, the participants were randomly assigned to 3 groups. The first group was given a “data view” on five unicorn companies whose founders graduated from college (Brian Acton, Biz Stone, Martin Lorenzon, Eduaro Saverin, Garrett Camp). The second group was given a “data view” of five unicorn companies whose founders dropped out of school (Travis Kalanick, Jan Koum, Jack Dosey, Mark Zuckerberg, Deniel Ek). The two groups were actually viewing data on the same five companies (Uber, WhatsApp, Twitter, Facebook, and Spotify). The last group was not given the same data as the previous two groups (control group).

As a result, 87% of participants in the first group bet on startups whose founders graduated from college. In contrast, this figure in the second group is only 32%, which is 55% lower. This means that the success stories people hear have a huge impact on the beliefs and decisions of the participants. Both groups were significantly different from the control group that did not “see data”. This group bet 47% on startups whose founders graduated from college. In fact, the researchers simulated a predictive dataset such that the unicorn ratio between the two groups whose founders graduated and dropped out of college were approximately the same.

Participants in all groups were very confident in their judgments (albeit contradictory). In the “data-viewed” groups, participants even came up with “causal” explanations that were consistent with the data they were viewed: for example, persistent college graduates (group 1) and creative dropouts (group 2). Notably, they were all aware that the data they were initially viewed was biased in one color (only founders graduated or dropped out).

This study shows that correct but biased information has a huge impact on readers’ beliefs.

The article is adapted from the study “Success stories cause false beliefs about success” by George Lifchits, Ashton Anderson, Daniel G. Goldstein, Jake M. Hofman and Duncan J. Watts published in “Judgment and Decision Making” (2021) )

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