Understanding the void

As a data scientist or a quality professional, one should understand the whole picture. Sometimes this means that you have to gain information from what is there as well as what is not there. I like to call this the void.

A great story that comes to my mind regarding this is from a talk from Jeffrey S. Rosenthal. He also posted this in a great article called “I am biased, You are biased“.

” During World War II, the U.S. Air Force wanted to strategically reinforce the hull plating of its fighter planes to better withstand enemy fire — but which parts of the plane should be reinforced? Charts and graphs were carefully constructed, showing the location of bullet holes on returning aircraft. The military then decided to consult a statistician — always a clever move. Professor Abraham Wald immediately realised that those graphs were based on a biased sample: they only included data for the planes which actually returned from battle. The real issue was the location of bullet holes on the planes which were shot down and never made it home. The military wisely followed Wald’s advice, to reinforce those parts of the hull that came back clean and bullet-free — those were the places where any shots would be fatal”

Keep on learning…

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