Confidence Interval: Advantages, Disadvantages and the Dilemma of Interpretation

Confidence interval (CI) is one of the important reporting tools for research data as it not only provides valuable information about the effect size along with its width but also possible clinical significance. Unfortunately, this approach is not being utilized to its fullest extent. Determining point estimate always includes an element of uncertainty due to associated sampling error. A confidence interval may be an appropriate tool to measure this uncertainty. Further, the P value does not convey information about the magnitude of an effect and the error associated with it. Thus, in an ideal situation effect size should preferably be associated with a confidence interval to assess precision. Not only does CI let us assess likely effects but also decides whether the intervention applied could have clinical utility. In contrast, the p-value limits our option to either reject any differences that are not significant or accept those that are. However, confidence intervals are commonly misinterpreted. It is imperative to understand that the CI is not the range of effects that 95% of patients in the population exhibit. Moreover, it would also be erroneous to say that there is a 95% probability that the CI includes the true population effect. Interpretation is usually based on the context in which the confidence interval is being looked at. From a utility point of view and like other statistical tools confidence interval approach does have several advantages as well as disadvantages and is far beyond being a perfect statistical tool.