Confidence interval (CI) There is always some uncertainty in research. This is because a small group of patients is studied to predict the effects of a treatment on the wider population. The confidence interval is a way of expressing how certain we are about the findings from a study, using statistics. It gives a range of results that is likely to include the true value for the population. The CI is usually stated as 95% CI , which means that the range of values has a 95 in a 100 chance of including the true value. For example, a study may state that based on our sample findings, we are 95% certain that the true population blood pressure is not higher than 150 and not lower than 110 . In such a case the 95% CI would be 110 to 150. A wide confidence interval indicates a lack of certainty about the true effect of the test or treatment often because a small group of patients has been studied. A narrow confidence interval indicates a more precise estimate (for example, if a large number of patients have been studied). Confounding factor Something that influences a study and can result in misleading findings if it is not understood or appropriately dealt with. For example, a study of heart disease may look at a group of people that exercises regularly and a group that does not exercise. If the ages of the people in the 2 groups are different, then any difference in heart disease rates between the 2 groups could be because of age rather than exercise. Therefore age is a confounding factor. Consensus methods Techniques used to reach agreement on a particular issue. 