How to Calculate Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value

Co-authored by Mario Banuelos, PhD

Last Updated: May 27, 2025 Approved

If you're screening for a disease or specific characteristic in a group of people, it's important to know the sensitivity, specificity, positive predictive value, and negative predictive value so you know how useful your test is.^{[1]
X
Trustworthy Source

PubMed Central
Journal archive from the U.S. National Institutes of Health

Go to source} We'll help you learn how to calculate these values to ensure your results are as accurate as possible.

Calculator

Sensitivity and Specificity Calculator

Method 1

Method 1 of 1:

Doing Your Own Calculation

Download Article

1000 patients in a clinic.
syphilis.^{[2]
X
Trustworthy Source

PubMed Central
Journal archive from the U.S. National Institutes of Health

Go to source}

Advertisement
darkfield microscopic documentation of the presence of the Treponema pallidum bacteria from scrapes off a syphilitic sore, in collaboration with clinical findings. Use the gold standard test to determine who has the character and who does not.^{[3]
X
Trustworthy Source

PubMed Central
Journal archive from the U.S. National Institutes of Health

Go to source} For illustration, let us say 100 people have it and 900 do not.
Have a test that you are interested in determining its sensitivity, specificity, positive predictive value, and negative predictive value for this population, and run this test on everyone within the chosen population sample. For example, let this test be a rapid plasma reagin (RPR) test to screen for syphilis. Use it to test the 1000 people in the sample.^{[4]
X
Trustworthy Source

PubMed Central
Journal archive from the U.S. National Institutes of Health

Go to source}
For people that have the characteristic (as determined by the gold standard), record the number of people who tested positive and the number of people who tested negative. Do the same for people that do not have the characteristic (as determined by the gold standard).^{[5]
X
Research source} You will end up with four numbers. People with the characteristic AND tested positive are the true positives (TP). People with the characteristic AND tested negative are the false negatives (FN). People without the characteristic AND tested positive are the false positives (FP). People without the characteristic AND tested negative are the true negatives (TN)^{[6]
X
Trustworthy Source

PubMed Central
Journal archive from the U.S. National Institutes of Health

Go to source} For example, let us suppose you did the RPR test on the 1000 patients. Among the 100 patients with syphilis, 95 of them tested positive, and 5 tested negative. Among the 900 patients without syphilis, 90 tested positive, and 810 tested negative. In this case, TP=95, FN=5, FP=90, and TN=810.
In the case above, that would be 95/(95+5)= 95%. The sensitivity tells us how likely the test is to come back positive in someone who has the characteristic.^{[7]
X
Research source} Among all people that have the characteristic, what proportion will test positive? 95% sensitivity is pretty good.
In the case above, that would be 810/(90+810)= 90%.^{[8]
X
Research source} The specificity tells us how likely the test is to come back negative in someone who does not have the characteristic. Among all people without the characteristic, what proportion will test negative? 90% of specificity is pretty good.
In the case above, that would be 95/(95+90)= 51.4%. The positive predictive value tells us how likely someone is to have the characteristic if the test is positive.^{[9]
X
Research source} Among all people that test positive, what proportion truly has the characteristic? 51.4% PPV means that if you test positive, you have a 51.4% chance of actually having the disease.
In the case above, that would be 810/(810+5)= 99.4%. The negative predictive value tells us how likely someone is to not have the characteristic if the test is negative.^{[10]
X
Research source} Among all people that test negative, what proportion truly does not have the characteristic? 99.4% NPV means that if you test negative, you have a 99.4% chance of not having a disease.

Community Q&A

Search

Add New Question

Question

Do I need a random sample to calculate positive predictive value?

Community Answer

No, you can still calculate knowing which group received which treatment.

Thanks! We're glad this was helpful.
Thank you for your feedback.
If wikiHow has helped you, please consider a small contribution to support us in helping more readers like you. We’re committed to providing the world with free how-to resources, and even $1 helps us in our mission. Support wikiHow
Yes No

Not Helpful 1 Helpful 1
Question

How is sensitivity related to positive predictive value by formula?

Community Answer

Lighting, sound, smell, aroma, etc. would need further information to create a formula based on what it is you want to achieve.

Thanks! We're glad this was helpful.
Thank you for your feedback.
If wikiHow has helped you, please consider a small contribution to support us in helping more readers like you. We’re committed to providing the world with free how-to resources, and even $1 helps us in our mission. Support wikiHow
Yes No

Not Helpful 17 Helpful 3

Ask a Question

200 characters left

Include your email address to get a message when this question is answered.

Submit

Video

Tips

Accuracy, or efficiency, is the percentage of test results correctly identified by the test, i.e. (true positives + true negatives)/total test results = (TP+TN)/(TP+TN+FP+FN).

Thanks
Helpful 0 Not Helpful 0
Try drawing out a 2x2 table to make things easier.

Thanks
Helpful 44 Not Helpful 8
Know that sensitivity and specificity are intrinsic properties of a given test, and do not depend on the given population, i.e. these two values should be the same when the same test is applied to different populations.

Thanks
Helpful 19 Not Helpful 4