Confidence intervals provide a richer description of study results than p-values alone by indicating both the direction and magnitude of observed effects and the uncertainty surrounding those estimates. A 95% confidence interval represents a range such that if the study were repeated many times using the same methodology, approximately 95% of the calculated intervals would contain the true parameter value. The width of the interval reflects the precision of the estimate, with narrower intervals indicating more precise estimation.

Interpretation of confidence intervals in clinical trials typically focuses on whether the interval includes clinically important values, particularly the null value representing no effect. When the 95% confidence interval excludes the null value, the result is statistically significant at the 0.05 level. However, confidence intervals provide additional information beyond significance testing by indicating the range of effects consistent with the observed data. An interval that excludes the null but lies entirely within a range of clinically trivial effects suggests statistical significance without clinical importance.

The boundaries of confidence intervals help assess the plausibility of clinically meaningful effects. When designing non-inferiority or equivalence trials, confidence intervals are directly compared against pre-specified margins defining acceptable performance. In superiority trials, the lower bound of the confidence interval indicates the smallest treatment benefit consistent with the data, enabling assessment of whether the treatment effect is likely to be clinically worthwhile even at the lower end of the plausible range.