A classic 1978 article in the New England Journal of Medicine reveals this problem. The researchers asked 60 Harvard physicians and medical students a seemingly simple question: If a test to detect a disease with a prevalence of 1/1,000 has a false positive rate of 5%, what is the chance that a person found to have a positive result actually has the disease?
Only 14% gave the correct answer of 2% with most answering 95%.
Base rate fallacy/false positive paradox is derived from Bayes theorem. When the incidence of a disease in a population is low, unless the test used has very high specificity, more false positives will be determined than true positives. The difference in the numbers can be quite striking and certainly not inherently understandable.
= your doctors do not understand statistics