The year is 1943. The fight for occupied Europe could be decided in the skies. But Allied planes are too vulnerable to enemy fire.
Enter Abraham Wald, member of the Statistical Research Group, a team of the best statisticians in the world.
His task: to figure out the right place to install new armour on Allied fighter planes.
Wald and a team of mathematicians and engineers inspected all the aircraft that had taken heavy fire in combat.
Basically, they counted the bullet holes.
Calculating the number of holes per square foot, they observed that the tail of each plane had taken more hits than the plane’s engine.
So the best place to add more armour would be the tail of each plane, right?
“Wrong!” said Mr Wald.
But why?
“We are counting the planes that returned from a mission.
Planes with lots of bullet holes in the engine did not return at all.”
Wald had spotted a case of ‘Survivorship bias’.
The Airforce were only paying attention to what they could see – the survivors.
The data they couldn’t see, told a very different story.
So whenever you’re working with data, don’t just accept what’s there.
To get the full picture, keep on asking: what are you missing?