A confidence interval provides a range of values within which the true population parameter is likely to fall, with a specified level of confidence (usually 95%). Unlike hypothesis tests, confidence intervals offer an estimate of the parameter's possible values and the precision of the estimate. For instance, a 95% confidence interval suggests that if the same population is sampled multiple times, approximately 95% of the calculated intervals will contain the true parameter. (Guetterman, T. C., (2019). In health care research, these tools are essential for assessing interventions, treatment efficacy, and patient outcomes. For example, researchers might use a hypothesis test to determine whether a new drug reduces post operative nausea and vomiting more effectively than an existing one. A significant p-value would indicate that the new drug has a statistically significant effect