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Definitions of Statistical Terms

Percent - Percent literally means the number (of cases or people) per 100. One out of 100 equals 1%; 50 out of 100 equals 50%, or half of the total number; 25% is a quarter of the total, or one in four; 75% is three out of every four in a group or 75 out of 100.

Incidence - Incidence refers to the number of new cases of a disease or condition. If the disease tends to last a very short time (as pancreatic cancer does), incidence and prevalence are close (see definition of prevalence below). If the disease lasts a long time, prevalence is greater than incidence (as with asthma).

Prevalence - Prevalence refers to the number of existing cases of a disease. That includes those who have it and those newly diagnosed with it.

Risk factor - A risk factor is something that raises or increases the possibility that you will suffer from a disease or condition. For example, having a family history of high blood pressure (for example, your mother has high blood pressure), being overweight, and smoking are all risk factors for high blood pressure. Having all these risk factors makes it even more likely you will have high blood pressure. High blood pressure (or hypertension), in turn, is a risk factor for stroke, kidney damage, and heart disease.

Sensitivity - Sensitivity is one measure of how good a test is. It is the number of "true positives" plus "false negatives," divided by the percent of cases picked up by the test. A "true positive" is an accurate positive reading-for example, the test says you're pregnant and you really are. On the other hand, a "false negative" test result is one that comes back negative but should have been positive. The test says you're not pregnant when in fact you are. Sensitive tests pick up the most cases.

Specificity - Specificity is another measure of the effectiveness of a test. It is defined as the number of "true negatives" plus the number of "false positives" divided by the percent of negative results that are really negative. Specific tests don't give many false positives AS LONG AS THE DISEASE IS NOT REALLY RARE; for example, syphilis tests, despite being fairly good, generate almost as many false positives as true positives, when used as a screening test.

Remember that many studies may claim a "correlation," or relationship between two things. However, any good statistician will tell you that "correlation does not imply causation." What this means is that too many other factors may be contributing to the results for us to truly say that one thing causes another.

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Last Reviewed: Jul 26, 2001

Kenneth   Davis, Jr, MD, FACS Kenneth Davis, Jr, MD, FACS
Professor of Surgery and Clinical Anesthesia
College of Medicine
University of Cincinnati