Aspiring entrepreneurs hear it often, dropping out of college is the key to success.
After all, it worked for Steve Jobs, Bill Gates and Mark Zuckerberg. They’re all rich.
These business moguls’ well-known stories give the impression that to become a business titan all you need is a big idea in college and the courage to quit school to pursue it.
The challenge with this narrative is that college dropouts usually don’t become billionaires
There are far more budding entrepreneurs who dropped out of college to start companies and failed than those who succeeded.
Welcome to Survivorship bias…
It’s a cognitive shortcut that occurs when a successful subgroup is mistaken as the entire group, due to the invisibility of the failure subgroup.
Here’s another example for you…
2,000 years ago, the Roman philosopher and politician, Cicero, told the story of an atheist named Diagoras, who had been vocal in his non-belief about the gods.
Those trying to convince Diagoras of the existence of higher powers showed him a series of painted tablets that portrayed a group of sailors who had prayed during a vicious storm and then survived the shipwreck.
Diagoras looked at the paintings and replied, “I see those who were saved, but where are those painted who prayed and drowned?”
So how can you avoid this trap so you don’t make decisions based on
faulty logic that the survivorship bias produces?
In an article in Scientific American it quotes Sendhil Mullainathan, a professor of computation and behavioral science at the University of Chicago Booth School of Business, who has thought a lot about how to avoid such logical errors and offers this advice…
Look at your life and where you get feedback and ask, “Is that feedback selected, or am I getting unvarnished feedback?”
Whatever the claim—it could be “I’m good at blank” or “Wow, we have a high hit rate” or any sort of assessment—then you think about where the data comes from. Maybe it’s your past successes. And this is the key: Think about what the process that generated the data is. What are all the other things that could have happened that might have led me to not measure it? In other words, if I say, “I’m great at interviewing,” you say, “Okay. Well, what data are you basing that on?” “Well, my hires are great.” You can counter with, “Have you considered the people who you have not hired?”
It’s a very simple thing, where you just need to ask the question: What’s the data that’s not present?
So the next time you make a decision based on some data from someone or some report, ask yourself what data is NOT present in these results that might lead me to the wrong conclusion.
You’ll be smarter for it.
Your move.
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