What does a narrow confidence interval (CI) indicate about an estimated effect?

• A. Greater precision
• B. Less variability
• C. Higher likelihood of a false positive
• D. Wider range of outcomes

Answer: (A)

A narrow confidence interval (CI) indicates greater precision in the estimated effect. When a confidence interval is narrow, it suggests that the range of values within which the true effect is likely to lie is small, reflecting a high level of certainty regarding the estimate. This means that the estimate is likely close to the true population parameter, making it a more reliable measure.

For example, in clinical trials or observational studies, a narrow CI around a point estimate (like a mean difference or an odds ratio) implies that the point estimate has been calculated from a sufficient amount of high-quality data, leading to more confidence in the results. Researchers can trust that the estimated effect is not widely varying and is robust against random sampling variability.

In contrast, other options present notions that do not align with the implication of a narrow CI. For instance, a narrow CI does not directly relate to less variability in the data itself or suggest a greater likelihood of false positives. Additionally, a narrow CI does not indicate a wider range of outcomes; rather, it narrows down the possible values for the estimated effect. Thus, focusing on the greater precision provided by a narrow CI is crucial for interpreting the reliability of findings in evidence-informed practice.