In everyday terms, a confidence interval is the range of values around a sample statistic (such as mean or proportion) within which clinicians canexpect to get the same results if they repeat the study protocol or intervention, including measuring the same outcomes the same ways. As you askyourself, “Will I get the same results if I use this research?”, you must address the precision of study findings, which is determined by the ConfidenceInterval. If the CI around the sample statistic is narrow, you can be confident you will get close to the same results if you implement the same researchin 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 foundthat tai chi affected sleep quality in older people. If, according to your study, you found the lower boundary of the CI to be .49, the study statistic to be0.87, 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 CI.(UB + LB)/2 = Statistic [(1.25 + .49)/2 = .87]Keep in mind that a mean difference of 0 indicates there is no difference; this CI does not contain 0. Therefore, the sample statistic is statisticallysignificant 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 onthe sample statistics and the CI, clinicians could now use your study and confidently include tai chi exercises among possible recommendations forpatients who have difficulty sleeping.Now you can apply your knowledge of CIs to create your own studies and make wise decisions about whether to base your patient care on aparticular research finding.
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