False Positive/Negative Rate: Which Is Better? Why? What About Predictive Value?

Too many questions!


A COVID-19 at home rapid test shows two lines, one on the test line and one on the control line. This is a positive test.
Is this result reliable?

It’s the age-old question of laboratory tests and analyses, “How accurate is this?” The answer to this question is always, “It depends…” Some lengthy explanation follows this answer of what is best for the person being tested. When it comes to individual medical decisions, these discussions are best when had by a healthcare provider and the patient, not the patient and Google. But what about a question at the population level?

Take, for example, influenza surveillance. When I started working at a state health department, one of the first things I did was contact clinical laboratories and ask them to provide the number of rapid influenza tests and their results. This would help me inform the public and public health workers of when and where influenza was active. But I had to remember the performance of these tests, as well as the prevalence (the existing cases of a disease) of influenza in the places where the tests were being done.

The rule of thumb is: If prevalence is low, then the false positive rate will be high. If prevalence is high, then the false negative rate will be high. It’s all based on math, and how that math breaks down on a 2×2 table based on a test’s sensitivity and specificity. Sensitivity is the probability that the test will detect a disease when the disease is there. Specificity is the probability that the test will be negative when there is no disease.

Let’s say a test is 99% sensitive and 99% specific. That’s pretty good, right? It will catch 99% of all true cases with a positive test, and it will rule out 99% of non-cases with a negative test. Know that there are four categories being looked at: TRUE positives, FALSE positives, TRUE negatives and FALSE negatives. As prevalence increases, the chance that a positive test is true increases. You have more true positives. The chance of a false positive decreases. Likewise, the chance of a negative result being a true negative decreases as prevalence increases.

So we go back to the question of what you want to achieve… If you are a physician and want to catch the most cases, then you want the patients you’re testing to be in a group with the highest prevalence. This is why healthcare…



René F. Najera, MPH, DrPH

DrPH in Epidemiology. Associate/JHBSPH. Adjunct/GMU. Epidemiologist. Father. Husband. (He/Him/His/El)