We see this “Surviving” population as somehow special when in actual fact it might have got there through no more than sheer ass.
It all started with a random letter from a stockbroker suggesting that a certain stock was guaranteed to increase significantly in value. You are too clever to be fooled of course, but you discreetly keep and eye on the value and sure enough, a few weeks later it has increased. The next letter predicts another stock to drop in value — true to form it does go down. Probably just lucky you tell yourself. But by the time the 6th letter arrives and correctly predicts a stock movement you are just wanting to know where to throw your money. 6 correct stock picks — the odds of that happening must be very small indeed, it must be more than luck…
Of course you would be sadly mistaken and soon parted with your money if you get sucked in. The trick is that you are not the only one receiving letters. Initially say 10,000 people will receive a note — half saying a stock will go up and the other half to go down. Those who receive the correct guess will receive another letter, again half predicting up and the other half a drop. By the end of the series there are still 156 people who have received the correct sequence of picks. No luck required, just a big enough starting sample!
The problem for us is that we have a hard time seeing that these unlikely events can simply occur through chance. We see this “Surviving” population as somehow special when in actual fact it might have got there through no more than sheer ass.
You succumb to survivorship bias because you are innately terrible with statistics. For instance, if you seek advice from a very old person about how to become very old, the only person who can provide you an answer is a person who is not dead. The people who made the poor health choices you should avoid are now resting in the earth and can’t tell you about those bad choices anymore.
We get caught out by survivorship bias in all sorts of contexts. Our retirement fund performance; the unicorn business; the winning football team. Frequently we ascribe a lot of significance to the last one standing, studying it closely and copying their every action. This can lead to genuine insights and performance improvements, but it can also lead us astray as there is a fair chance that the leader is there through luck — someone has to be on top after all!
A story from WWII illustrates how easily it can be for even experts to get caught out.
Back during World War II, the Allies were engaged in a brutal bombing campaign against German cities.As one might expect,
Germans didn’t enjoy being bombed very much. German guns were doing their best to shoot down Allied aircrafts, and preventing losses fast became a major issue.
It was apparent that planes needed more armour to make them sturdier against German fire. But the more armour you added, the heavier the plane got, which meant higher fuel costs and diminished range.
So it became very important to know exactly where on a plane to add armour in order to have the highest return on investment without weighing down the machines.
The military looked at the bombers that had returned from enemy territory. They recorded where those planes had taken the most damage. Over and over again, they saw the bullet holes tended to accumulate along the wings, around the tail gunner, and down the center of the body. Wings. Body. Tail gunner. Considering this information, where would you put the extra armour?
The obvious solution would be to put the armour in the spots featuring the most bullet holes. But army statistician Abraham Wald realised that the military was looking at a biased sample: one that only included plane that made it back from their mission.
In other words, none of the bullet impacts they could now observe had been critical enough to shoot down the plane. Consequently, these impacts did not correspond to the spots in most dire need of being reinforced, and it followed that these spots were actually the ones without any bullet impacts.
Are you being fooled anywhere by survivorship bias?