The meaning of a confidence interval (CI) and why a confidence interval adds valuable information

Sample Answer

A confidence interval (CI) is a range of values, derived from a sample of data, that is likely to contain the true value of an unknown population parameter. It provides an estimated range of values which is likely to include an unknown population parameter, the estimated range being calculated from a given set of sample data. It's typically expressed with a confidence level, such as 95% or 99%. For example, a 95% CI means that if you were to take many samples and calculate a CI for each, 95% of those intervals would contain the true population parameter. 📊

A CI adds valuable information to study data that a Doctor of Nursing Practice (DNP) examines for several key reasons:

Quantifies Precision and Uncertainty: While a p-value tells a DNP whether an observed effect is statistically significant (i.e., unlikely due to chance), it doesn't convey the magnitude or precision of that effect. A CI, on the other hand, provides a range within which the true effect likely lies. A narrow CI indicates a more precise estimate of the effect, suggesting less variability and greater confidence in the observed result. Conversely, a wide CI indicates a less precise estimate, implying more variability or a smaller sample size. This precision is crucial for DNP-led practice changes. 📏