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Survivorship Bias and 4 Examples of How It Can Distort Your Thinking

Survivorship Bias and 4 Examples of How It Can Distort Your Thinking

There are many cognitive biases that influence our decisions and judgements, but have you ever heard of survivorship bias? I hadn’t until I read about a particular problem and a very clever man who solved it. Here’s the problem. It’s 1943 and we are in the middle of WWII.

The US is having to deal with an alarming number of returning fighter planes riddled with bullet holes. Experts are bought in to advise the military.

They are told they need to protect the weak spots on the planes but obviously can’t cover them all with additional metal reinforcements. For a start, the planes won’t get off the ground and metal is in short supply. Every returning plane shows a pattern of devastating bullet hole damage on the wings, the tail and the body. So my question to you is – where will you upgrade the metal protection on these planes? If you think the answer is along the damaged sections of the plane, congratulations, you’ve just succumbed to survivorship bias. So what am I talking about? The plane dilemma highlights the problems of focusing on the survivors and not taking into account those who didn’t make it back. Abraham Wald was a gifted Hungarian statistician and one of the experts advising the military during WWII. In fact, the military wanted to put the extra protection on the damaged sections of the planes, but Wald said no. This would be a dreadful mistake. Why? Because these planes survived, they came back, they returned.

The damage showed exactly where a plane could be hit and still fly.

The wings, tail and body were the strongest parts of the plane and didn’t need additional protection. It was the planes that didn’t return that were important. Because these were the ones that failed. You don’t need to protect the planes that return for that one simple reason – they returned. Once Wald examined the planes that went down, he discovered that the engines sustained the majority of the damage. Wald deduced that once an engine was hit, there was no chance the aircraft could survive.

Therefore, this is where the reinforcements should be placed, to protect the engines. So how does survivorship bias affect your everyday decisions? The main premise of survivorship bias is that we focus on successes in order to become successful. But successes are only half of the story. We also need to look at how things fail if we want to succeed. In Wald’s case, everyone around him focused on the planes that made it home. Once you realise the mistake in the plane scenario, it is easy to see how you can become focused on the wrong thing.

The problem is, the world is full of successful people trying to sell us their own particular stories of triumph. And we tend to gravitate to them. After all, no one wants to sit in a theatre and listen to the actor that failed, or watch a Ted Talk about the entrepreneur who couldn’t fund her start-up. We soak up success stories because we want the tips, the nuggets of advice, the words of wisdom so that we can replicate them and achieve something in our own lives too. But shouldn’t we also listen to those that didn’t make it? Aren’t their stories equally as important? You might think by this point that I’m being quite negative. But consider this, how much of success really comes down to luck or circumstance? Twenty years ago, who could have predicted that we’d all be walking around with plastic cups of coffee in our hands? It could just as well have been tea? Or hot chocolate? Perhaps there were tea and hot chocolate shops, but they failed. So if that’s true, anyone wanting to start up a new hot beverage company should need to know why. Let’s now turn our attention to real life.

The most common modern-day example of survivorship bias can be found on social media and it’s the ubiquitous selfie. When you see all those perfect pictures, the pouting lips, the long eyelashes, the chiselled cheekbones, and you think to yourself ‘how can someone be that gorgeous?’ The answer is, they’re not.

They have taken tens of hundreds of pictures, picked the best one, and then highly edited it to make themselves look perfect. Trust me, you’ll never see the ones where they look normal. You are studying at university and when you graduate, you want to start up a computer or social media company. You start researching everything you can on Steve Jobs, Mark Zuckerberg and Jack Dorsey. Subsequently, you find out that they all dropped out of college to pursue their dreams. Does that mean you should drop out too? These three men are the exception, not the rule. In order to get a good job, you should carry on with your education, not drop out. You read in the papers about yet another jackpot lottery winner, so you decide to go and buy a few tickets, hoping you’ll be next. Of course, it’s lovely to hear when ordinary people win life-changing amounts of money, but what about the millions that purchased a ticket and didn’t win? And if you think that people don’t really believe they will win when they buy a ticket, a survey in 2013 found that 3.6 million UK workers were counting on a lottery win to fund their pensions. This is despite the fact that the odds of winning are more than 14 million to one. Let’s say you are running a company and you have a customer complaints department. You focus your energy on those who are complaining. Although it is right that you’re dealing with customer complaints, typically, these only make up around 10% of your business. So are you neglecting the customers that are happy with your product? These are the ones that are not complaining, so what are you doing right? Focus on that instead. Survivorship bias can skew our thinking in many ways. It can make us ignore failures, which are just as important to truly understand the successes. It can also fool us into thinking success is easy to achieve. Moreover, it leads us to believe that the few represent the many, when in fact, we should be looking at the majority. So remember, to get a much-balanced picture, don’t forget about failure. R.

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