Compare p-values and confidence intervals in interpreting cohort study findings.

• A. P-values indicate how likely the result is due to chance; CIs indicate the precision and plausible range of the effect
• B. P-values measure the magnitude of the effect; CIs measure the direction of association
• C. P-values and CIs provide the same information
• D. P-values determine clinical significance; CIs determine statistical significance

Answer: (A)

The key idea is that p-values and confidence intervals provide different pieces of the evidence from a cohort study. A p-value tells you how unlikely the observed data would be if there were no real effect (the null hypothesis). It’s about the strength of evidence against no association, but it doesn’t tell you how big the effect is or how precisely it’s estimated.

A confidence interval, on the other hand, shows the range of true effect sizes that are plausible given the data, and it reflects the precision of the estimate. In a cohort study reporting a relative risk or hazard ratio, the interval tells you not only the direction and magnitude you might expect in the population, but also how much uncertainty surrounds that estimate. If the interval excludes the no-effect value (for relative risk or hazard ratio, a value of 1), that suggests statistical significance at the chosen level; if it includes the no-effect value, the result is not statistically significant at that level.

So, the statement that p-values indicate how likely the result is due to chance and confidence intervals indicate the precision and plausible range of the effect captures the distinct roles: p-values assess evidence against the null, while confidence intervals convey the magnitude and precision of the estimated effect.

Notes on why other descriptions aren’t right: p-values do not measure the size of the effect, nor do they indicate direction alone; confidence intervals do more than indicate direction—they provide a range for the true effect and its precision. And clinical significance is a separate consideration from statistical significance; a result can be statistically significant but of limited clinical importance, or vice versa.