Which statistic best conveys the precision and potential magnitude of the observed effect alongside p-value?

• A. Confidence interval
• B. Sample size
• C. P-value
• D. Median with interquartile range

Answer: (A)

The statistic being tested here is the confidence interval. It captures both the estimated size of the effect and how precisely that size is known. The interval gives a range of plausible values for the true effect (for example, a risk ratio or mean difference) at a chosen confidence level, commonly 95%. The width of this interval reflects precision: a narrower interval means greater precision, while a wider interval indicates more uncertainty about the true magnitude. Importantly, the interval also conveys practical or clinical magnitude because you can see the range of plausible effect sizes directly, and whether that range excludes no effect.

P-values, by contrast, only tell you whether the observed effect is unlikely under the null hypothesis; they do not convey how large the effect might be or how precisely it has been estimated. Sample size affects the precision (larger samples tend to yield narrower intervals, all else equal) but is not itself a measure of the effect’s magnitude. A median with interquartile range describes the data’s center and spread but not the population-level effect size or its precision in the same sense as a confidence interval.

So, a confidence interval best communicates both how big the observed effect could reasonably be and how precisely that estimate has been determined, complementing the information provided by the p-value.