How should confidence intervals be interpreted for relative risk?

• A. A 95% CI for RR indicates the range of values compatible with the data; if it includes 1, the association may be non-significant at that level
• B. A 95% CI for RR guarantees the true effect lies within the interval
• C. A 95% CI for RR equals the p-value
• D. A 95% CI for RR shows the probability that the null is true

Answer: (A)

Confidence intervals for relative risk show the range of plausible values for the true relative risk based on the observed data and the statistical model used. A 95% interval means that if we repeated the study many times and computed a new interval each time, about 95% of those intervals would contain the true relative risk. The practical takeaway is that if the interval includes 1, the data do not provide statistically significant evidence of an association at the 0.05 level. If the interval lies entirely above 1 or entirely below 1, there is evidence of an association in that direction.

Note that a confidence interval does not guarantee the true effect lies within this single study’s interval, and it is not the same as a p-value. The p-value assesses the probability of observing the data under the null hypothesis, and while a p-value below 0.05 often corresponds to a CI that excludes 1, the two are related but distinct concepts. The interval also does not assign a probability to the null hypothesis being true.