Why False Positives Matter
A test can be described as 99% accurate and still create many wrong positive results. The reason is prevalence. Prevalence tells how common the condition is in the tested group. When the condition is rare, most people tested do not have it. Even a small one percent false positive rate can affect many healthy people.
Base Rate Effect
This calculator shows the base rate effect. It first estimates how many people truly have the condition. Then it estimates how many people do not have it. Sensitivity finds true positives among affected people. Specificity finds true negatives among unaffected people. The remaining unaffected people become false positives.
Reading the Result
The most useful number is often the false positive share of all positive results. A 99% specific test gives one false positive for every hundred unaffected people. If the condition affects only one percent of the population, true positives and false positives can be almost equal. That means a positive result may need confirmation.
Using Better Assumptions
The calculator lets you change prevalence, sensitivity, specificity, population, and follow up cost. This makes it useful for screening examples, classroom practice, workplace checks, quality control, and medical risk discussions. It is not a medical diagnosis tool. It is a statistics model.
Practical Interpretation
Use realistic prevalence for the group being tested. Do not assume the general population rate fits every subgroup. Also check whether the 99% accuracy refers to sensitivity, specificity, or total accuracy. These measures are different. A high sensitivity reduces missed cases. A high specificity reduces false alarms.
Decision Support
The results can guide communication. They show how many positives may be true, how many may be wrong, and how much confirmatory testing may cost. The CSV and PDF buttons help save the scenario. Always pair statistical output with expert judgment, reliable data, and context.
Common Mistake
Many readers focus only on accuracy. That can hide the number of healthy people in the tested group. A large healthy group can produce many false positives. For that reason, positive predictive value is often more helpful than accuracy alone. It answers a practical question. After a positive test, how likely is the result correct within this tested population today.