Enter your test's specificity, prevalence, and population size to calculate the number of false positives, true negatives, and the overall false positive rate. The False Positive Calculator breaks down how many healthy people are incorrectly flagged as sick, giving you a clear picture of diagnostic test accuracy.
False Positive Rate
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False Positives (count)
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True Negatives (count)
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Total Healthy People
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False Positives (% of all tested)
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False positives are healthy individuals who received a positive result on a diagnostic test — meaning they were incorrectly identified as having the disease. They represent one of four possible test outcomes alongside true positives, false negatives, and true negatives.
False positives are calculated using the formula: False Positives = (1 − Specificity) × Healthy Population, where Healthy Population = (1 − Prevalence) × Total Population. For example, with a specificity of 93% and a prevalence of 5% in a population of 10,000, there are 665 false positives.
True negatives are correctly identified healthy individuals. The formula is: True Negatives = Specificity × Healthy Population. Using the same example (specificity 93%, prevalence 5%, population 10,000), there would be 8,835 true negatives.
The false positive rate (FPR) is the proportion of healthy people who test positive. It equals 1 − Specificity. So a test with 93% specificity has a 7% false positive rate, meaning 7% of healthy people will incorrectly test positive.
The simplest formula is: False Positive Rate = 100% − Specificity. Alternatively, if you know the counts: FPR = False Positives / (False Positives + True Negatives) × 100%. Both methods yield the same result.
If specificity is 93%, the false positive rate is 100% − 93% = 7%. This means 7 out of every 100 healthy people tested will receive an incorrect positive result.
Yes. Higher prevalence means fewer healthy people in the population, which reduces the absolute number of false positives even if the false positive rate (1 − specificity) stays the same. Prevalence does not change the false positive rate itself, but it directly affects the count of false positives.
The false positive rate is the fraction of healthy people who test positive (= 1 − Specificity). The false discovery rate is the fraction of all positive test results that are actually false positives, which depends on both specificity and prevalence. This calculator focuses on the false positive rate derived from specificity.