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How can confidence intervals help determine whether a relative risk is statistically significant?

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In epidemiology, confidence intervals are used to estimate the precision of a relative risk and to determine its statistical significance.1

A relative risk represents the ratio of the probability of an event occurring in an exposed group versus a control group. To determine if this result is statistically significant, researchers examine whether the null value—which is 1.0 for relative risk—falls within the calculated confidence interval.12

If the confidence interval does not include 1.0: The result is typically considered statistically significant at the chosen confidence level (e.g., 95 percent). This suggests that the observed association is unlikely to be due to chance, as the range of plausible values for the true population parameter excludes the possibility of no effect.12
If the confidence interval includes 1.0: The result is not statistically significant. This indicates that the data are consistent with the null hypothesis, meaning there may be no true difference between the groups.12

It is important to remember that statistical significance does not equate to clinical or practical importance; it only indicates that the observed effect is unlikely to have occurred by random sampling error alone.2