Research projects often try to show that a condition (such as age) has a strong association with a health care outcome (such as death from heart disease). Confounding occurs when the health care outcome is influenced by factors other than the one that a researcher thinks is the most important. These factors are called confounders. For example, suppose that a researcher was studying whether pregnant women who drink coffee are more likely to have small babies than are women who do not drink coffee. Now suppose that the researcher found that the babies of coffee drinkers weighed less than the babies of women who did not drink coffee. However, the researcher failed to consider the possibility that women who drink a lot of coffee are also more likely to drink alcohol and use tobacco. In this case, alcohol and tobacco are potential confounders. In fact, drinking alcohol and smoking can lead to babies who weigh less at birth. If these behaviors are more common in coffee drinkers than in non–coffee drinkers, researchers say that these factors may “confound” the relationship between coffee drinking and birth weight. An analysis that uses special statistical methods to account for potential confounders might conclude that coffee drinking itself does not result in low birth weight. Adjustment for confounding is very important because it can prevent people from drawing incorrect conclusions about what information is important in patient care.