The old saying goes, “If at first you don’t succeed, try, try again.” But how many tries should you attempt before you throw in the towel and admit defeat? A new study from Northwestern University’s Kellogg School of Management entitled “Quantifying dynamics of failure across science, startups, and security” found that how you fail (and try, try again) determines if you’ll eventually succeed. It turns out that after an initial failure the paths diverge, and there’s a difference in the behavior of winners and losers.
“This is a crucial question because in our success-obsessed society failure is ubiquitous; almost every winner starts out as a loser,” says Dashun Wang, associate professor of management and organizations and lead author of the study. “If you can understand how people fail over and over and eventually succeed, you can identify a future winner while this person is still a loser.”
Wang and his team examined three distinct data sets: grant-seeking researchers, entrepreneurs, and terrorists. By tracing their attempts, the researchers were able to assess the characteristics of those who eventually achieve success compared with those who continued to fail.
“If we look at the human dynamic, there are two basic ways of thinking about why you fail: a chance model and learning model,” he says. “We quickly realized these simple models don’t offer the answer. It turns out to be a very complex prediction.”
What ultimately determines the individual’s path is the extent to which they learned from previous failures and how they applied that knowledge going forward. If someone uses the lessons to improve future attempts, it can lead to eventual success. However, if someone has too few failed attempts or they fail to incorporate the lessons, they will find themselves on the path to permanent failure.
The lessons of failure
Failure is an experience that gives a person two advantages: experience and feedback. “If you’ve done something in the past, you have experience and can start again without having to start from scratch,” says Wang. “You will probably have some sense of what worked and what needs to be improved, which is feedback. If you are able to incorporate it wisely, feedback is quite useful.”
Using a mathematical formula that assigns the number of tries as K, Wang found that the number of past experiences can impact success, but only when the person used the feedback and added an element of speed.
“If K is zero, it’s a chance mode, and if K is infinite, it’s a learning model,” he says. “What’s interesting is if you vary K, the whole process becomes not continuous but highly discontinuous. It would seem that if you learn a little from the past it’s better than learning nothing, but that’s not true. If you learn a little bit from the past but not enough, it’s just like not learning at all.”
The importance of speed
Wang found that there is a threshold of tries you must cross to predict success, and he likens it to the point where water turns to ice. “The critical threshold is 0.1 degree,” he says. “It’s failing faster and faster to eventually succeed.”
The idea of failing fast is common in Silicon Valley, where people are often successful when they fail faster and faster. Wang says you can identify a winner and a loser by looking at the quality of their failures.
“If you have two people who fail 10 times and one gives up and another tries and succeeds on try 11, traditional thinking is that the last attempt made a difference,” says Wang. “What the data shows is that it’s possible to tell very early by the two groups’ distinctive dynamics. Many other factors can determine if someone will succeed, but despite the complexity of the real-world setting, if we just use the current failing trajectory, we can achieve a respectable predictive power.”
Failing fast isn’t just prescriptive; it’s diagnostic, says Wang. “If you’re not failing faster and faster, you’re in stagnation region and not gaining enough feedback to form intelligent improvement,” he says.
When you try again it’s important to incorporate the feedback, but don’t overreact, says Wang. “Squarely focus on what needs to be improved,” he says. “The irony here is that people who failed in the stagnation region didn’t work less; they made unnecessary changes, throwing the baby [out] with the bathwater. Use feedback to know what needs to be improved, but retain what worked well.”