In everyday terms, a confidence interval is the range of values around a sample statistic (such as mean or proportion) within which clinicians can expect to get the same results if they repeat the study protocol or intervention, including measuring the same outcomes the same ways. As you ask yourself, "Will I get the same results if I use this research?", you must address the precision of study findings, which is determined by the Confidence Interval. If the Cl around the sample statistic is narrow, you can be confident you will get close to the same results if you implement the same research in your practice. Consider the following example. Suppose that you did a systematic review of studies on the effect of tai chi exercise on sleep quality, and you found that tai chi affected sleep quality in older people. If, according to your study, you found the lower boundary of the Cl to be .49, the study statistic to be 0.87, and the upper boundary to be 1.25, this would mean that each end limit is 0.38 from the sample statistic, which is a relatively narrow Cl. (UB+LB)/2 = Statistic [(1.25 + .49)/2 = .87] Keep in mind that a mean difference of 0 indicates there is no difference; this Cl does not contain O. Therefore, the sample statistic is statistically significant and unlikely to occur by chance. Because this was a systematic review, and tai chi exercise has been established from the studies you assessed as helping people sleep, based on the sample statistics and the Cl, clinicians could now use your study and confidently include tai chi exercises among possible recommendations for patients who have difficulty sleeping. Now you can apply your knowledge of Cls to create your own studies and make wise decisions about whether to base your patient care on a particular research finding.