A positive screening test does not always mean you have the disease. A negative test does not always mean you're free of it. Explore how prevalence, sensitivity, and specificity shape what your test result actually tells you.
Before diving into the simulator, let's build a foundation. Every screening test has four possible outcomes when applied to a population. These outcomes depend on two things: the test's accuracy and how common the disease is.
When a screening test is given to a group of people, every person falls into one of four categories:
| Has Disease | No Disease | |
|---|---|---|
| Test Positive | True Positive (TP) Correctly identified as having the disease |
False Positive (FP) Incorrectly told they have the disease |
| Test Negative | False Negative (FN) Disease missed by the test |
True Negative (TN) Correctly identified as disease-free |
TP ÷ (TP + FN)
Of all people who truly have the disease, what percentage does the test correctly identify as positive? High sensitivity means fewer false negatives — the test rarely misses a sick person.
TN ÷ (TN + FP)
Of all people who do NOT have the disease, what percentage does the test correctly identify as negative? High specificity means fewer false positives — healthy people are rarely told they're sick.
TP ÷ (TP + FP)
If your test is positive, what is the probability that you actually have the disease? This is what patients care about most. PPV depends heavily on prevalence.
TN ÷ (TN + FN)
If your test is negative, what is the probability that you truly do not have the disease? NPV also depends on prevalence — when disease is rare, even a mediocre test gives reassuring negatives.
Prevalence is the proportion of people in a population who have the disease at a given time. It is the single biggest factor that determines what a positive or negative result means for you.
Most people being tested are healthy. Even a small false-positive rate produces many false alarms relative to the few true cases. A positive result is less likely to be a true positive.
A substantial portion of the tested group has the disease. True positives outnumber false positives. A positive result is much more likely to be a true positive.
This is why screening programs target high-risk groups — the disease is more prevalent in those groups, so the test results are more meaningful.
Adjust the sliders to see how prevalence, sensitivity, and specificity interact to determine what test results mean. The population below is simulated with 1,000 people.
| Has Disease | No Disease | Total | |
|---|---|---|---|
| Test + | -- | -- | -- |
| Test − | -- | -- | -- |
| Total | -- | -- | 1,000 |
This curve shows PPV at every prevalence level for the current sensitivity and specificity. The dot marks your current setting.
These scenarios show why the same test can mean very different things depending on context.
Bob is 45 and doesn't inject drugs or have other Hepatitis C risk factors, but his doctor recommends screening. The local prevalence in his age group is 3%. The antibody test has about 99% sensitivity and 99% specificity.
If Bob tests positive, there's only about a 37% chance he truly has Hepatitis C. That's why a confirmatory test (RNA PCR) is always ordered after a positive antibody screen — most positive screens in low-prevalence populations are false alarms.
Rapid influenza tests have roughly 70% sensitivity and 95% specificity. In summer, influenza prevalence drops to about 1%. In winter, it rises to around 20%.
In summer: Most positive tests are false positives. Only about 12% of positive rapid tests are true influenza cases. Doctors should not trust a positive result in summer without confirmation.
In winter: A positive test is reliable (about 78% PPV). But 30% of true flu cases are missed (false negatives), so a negative test doesn't rule it out.
Mammography has about 85% sensitivity and 90% specificity. For women aged 40-49, breast cancer prevalence is roughly 1.5%.
In this scenario, only about 11% of positive mammograms represent actual breast cancer. Most callbacks are false alarms. This is why guidelines emphasize informed decision-making about when to begin screening — the anxiety from false positives is a real cost.
Modern HIV antibody/antigen combo tests have >99% sensitivity and >99.5% specificity. In a high-risk population (prevalence around 15%), a positive test has a PPV of about 97%.
This is why targeted testing in high-prevalence groups is so effective. The same test given to the general population (prevalence ~0.3%) would have a much lower PPV — around 37%.
Maria is 28 and has no identified HIV risk factors, but her clinician offers routine screening. The prevalence of HIV in her low-risk demographic is approximately 0.4%. The 4th-generation antigen/antibody test has approximately 99.9% sensitivity and 99.8% specificity.
Even with a near-perfect test, a positive result in this very low-prevalence group has a relatively low positive predictive value — meaning many positives will be false alarms. A confirmatory HIV-1/2 differentiation assay is always required before a diagnosis is made.
Linda carries a BRCA1 pathogenic variant and is enrolled in an intensive breast cancer surveillance program. Her estimated lifetime risk of breast cancer is approximately 55%. Mammography in BRCA mutation carriers has approximately 75% sensitivity and 93% specificity.
Because prevalence is so high in this group, a positive mammogram is far more likely to represent a true cancer than in average-risk women. However, the lower sensitivity means mammography alone misses roughly 1 in 4 cancers in BRCA carriers — which is why MRI is typically added to the surveillance protocol.