Understand why high-accuracy tests can still produce mostly false positives.
Test Parameters
Bayesian Analysis
If you test Positive, Probability you are Sick:
9.02%
Positive Predictive Value (PPV)
Population Breakdown (100,000)
Has Disease: 100
True Positive: 99
False Negative: 1
Healthy: 99,900
True Negative: 98,901
False Positive: 999
The Paradox: Even though the test is 99% accurate, out of 1,098 positive results, 999 are false alarms!
Overview
The false positive paradox occurs when false positive test results are more probable than true positive results, typically when the condition being tested for is rare.
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Pro Tips
Lower the prevalence to see the paradox in action.
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Fun Facts
"Even with a 99% accurate test, if a disease affects only 1 in 1000 people, a positive result implies only a ~9% chance of having the disease."
"This is why doctors often re-test or use a more specific second test."
