Why do we rely on specific information over statistics?
Imagine a medical test that is 99% accurate.
It sounds nearly perfect.
But if the disease is very rare—say, only 1 person in 1,000 has it—a positive result may still be more likely to be a false alarm than a true diagnosis.
Why?
Because the test is being used on a huge number of healthy people. Even a tiny false-positive rate can produce many incorrect positives.
The mistake people make is focusing on the dramatic new clue—“the test is positive”—while ignoring the base rate: how common the condition was to begin with.
This is the base rate fallacy: judging probability from a vivid piece of information while neglecting the background odds.
It appears everywhere.
A news story about a violent crime can make a neighborhood feel dangerous, even if crime remains rare.
A person from a prestigious college may seem certain to succeed, even though many capable people from elite schools still struggle.
The useful question is simple: Before this new clue appeared, how likely was the outcome?
That starting probability often matters more than our intuition admits.
