Positive predictive value: Lets imagine that we developed a test that can tell us with fair accuracy about a problem. It has - lets say for learning purposes sensitivity is 80% and specificity is 90%. Therefore if the test is done in a population of 20 people where the prevalence is 50%, 10 people truly have the disease and the other 10 do not.

Of the 10 who have the disease, only 8 will turn positive on the test (because the sensitivity is 80%).

Of the 10 who do not have the disease, only 9 will be negative (because the specificity is 90%).

Therefore we have a total of 8 true positives and 1 false positive = 9 positives. Positive predictive value is used to tell us that if a person tests positive, what are the chances that he truly has the disease. In this case, it is 8/9.

Now to understand negative predictive value, of all those who tested negative, how many are truly negative? Easy?!!

2 out of the 10 who have the disease actually came out negative on the test (because it was only 80% sensitive) and 9 of those who do not have the disease turned out negative (because the specificity is 90%). Therefore a total of 11. Now how many of those who turned negative on the test are truly negative? This is the negative predictive value.