False Positive Calculator
Provide sensitivity, specificity, prevalence, and population size to estimate how many false positives occur and their implications.
True positives: 1,900
False positives: 14,700
True negatives: 83,300
False negatives: 100
False positive rate: 15.0000%
Positive predictive value (PPV): 11.45%
Negative predictive value (NPV): 99.88%
How to Use This Calculator
- Enter test sensitivity and specificity (probabilities between 0 and 1).
- Provide disease prevalence (fraction of population affected).
- Set the population size to scale counts of true/false positives.
- Interpret the false positive rate, PPV, and NPV to assess diagnostic performance.
Formula
True positives = population × prevalence × sensitivity
False positives = population × (1 − prevalence) × (1 − specificity)
PPV = TP / (TP + FP)
NPV = TN / (TN + FN)
False positive rate = FP / (FP + TN)
Full Description
Even highly accurate tests produce false positives, especially when prevalence is low. This calculator highlights the potential burden of false alarms, aiding risk assessment, screening program design, and communication with stakeholders.
Predictive values emphasize how disease prevalence influences post-test probabilities, helping interpret positive or negative results meaningfully.
Frequently Asked Questions
Why does low prevalence increase false positives?
When few people have the condition, most positive results occur among the majority who are healthy, leading to more false positives even with good specificity.
Can I use percentages?
Yes. Enter probabilities as decimals (e.g., 0.02 for 2%). Outputs display percentages for clarity.
What if sensitivity or specificity is 1?
The calculator constrains values between 0.0001 and 0.9999 to avoid division-by-zero issues. Perfect tests rarely exist.
How can I reduce false positives?
Improve specificity, target high-prevalence populations, or use confirmatory testing strategies to mitigate downstream impacts.