The confidence interval (CI) is a crucial tool for reporting research data because it not only provides insight into the effect size and its range but also offers information about potential clinical significance. When determining a point estimate, there is always some uncertainty due to sampling error, and a confidence interval can help measure this uncertainty. Unlike the p-value, which does not indicate the magnitude of an effect or its associated error, the CI allows researchers to assess the precision of an effect size, which should be reported with confidence intervals to provide a clearer understanding of the findings. Confidence intervals help evaluate likely effects and determine whether an intervention may have clinical relevance. In contrast, the p-value simply informs us whether to reject or accept a null hypothesis based on statistical significance, without providing information about the strength of the relationship. It is essential to mention that the proper interpretation of a CI depends on the context in which it is analyzed.