Understand the incorrect classifications of patients and healthy people using sensitivity, specificity and prevalence

With increasing discussions on tests sensitivity, specificity and false positives and negatives, this page is providing the readers an opportunity to look at the correct and incorrect identification of patients using different tests.

Use the interactive cells to provide sensitivity, specificity and prevalence values to generate the false positive and false negative numbers.

Add values to the cells in green color
Sensitivity, Specificity and the prevalence needs to be provided as a fraction (95% as .95)

Prevalence is the expected number of patients in the population. If you believe that there are only 1 in 1000 people are having the disease, input should be .001

Calculate the false positives and negatives

Interpretation

True positives: People with the disease who have been correctly identified as positives by the tests

True negatives: People without the disease who have been correctly identified as not having the disease

False positives: Healthy people (not having the disease) who have been incorrectly identified as “positives” by the test

False negatives: People with the disease who have been missed by the test (who had incorrect “negative” results.