Interpreting treatment comparisons often hinges too heavily on p-values, even though they say little about the real-world importance of an effect. The more meaningful statistic is the 95% confidence interval, which shows the range where the true effect is likely to fall. A trial comparing sliding hip screws with cancellous screws illustrates this well: the reported reduction in re-operation risk is meaningful only when viewed alongside its confidence interval, which indicates how precise that estimate is. Sample size is the key driver—larger studies create narrower, more reliable intervals, while smaller studies leave wide uncertainty despite statistically significant p-values. Evaluating whether a confidence interval exceeds a clinically important threshold is essential for determining whether a result should influence practice.