The problem many people have with statistics is that misinterpreting one number can motivate entirely irrational and unwarranted reactions. Diagnostic tests show this phenomenon perhaps most clearly, as a positive test result suddenly seems like a life-ending piece of news. But a quick refresher of what the test really says may help calm your nerves.

All tests come with two capabilities, specificity and sensitivity. Specificity refers to how well the test avoids false positives — that is, it avoids diagnosing healthy people as sick. Sensitivity, meanwhile, refers to how well it picks up disease that is actually there. You want your tests to have high percentages of both. If you’re sick, you want the test to come back positive, and if you’re well you want it to come back negative.

But things aren’t so simple. The so-called positive and negative predictive values of a given test — the chance you have the disease, based on the result — are determined by the difference between how many people in a given study have the disease versus how many people in the general population actually have it. In other words, the number of people with the disease exists as a separate factor from the test’s specificity and sensitivity.

“If the people studied when a test is developed are sick, then the positive predictive values and negative predictive values only have meaning if we use the test on populations who are sick,” explains Dr. Aaron Carroll, professor of pediatrics at Indiana University. The reason people shouldn’t freak out over positive test results is that positive predictive values also lump healthy people into the mix.

For example, one study of mammograms found over 95 percent of people whose test came back positive did not have breast cancer. The test’s specificity and sensitivity were good, but it was testing mostly healthy people and only a small percentage of sick people. As a result, the chance a positive test result actually meant cancer was only 4.4 percent — hardly cause for a freak-out.