How should you interpret a confidence interval that crosses the null value?

• A. It indicates a strong treatment effect.
• B. It demonstrates a precise estimate with no uncertainty.
• C. It is not statistically significant at the chosen alpha; may reflect insufficient power or no effect.
• D. It proves the null hypothesis is true.

Answer: (C)

When a confidence interval crosses the null value, it means the data don’t provide statistically significant evidence of an effect at the chosen alpha level. The null value is the point of no effect (for a difference, zero; for a ratio, one). If that value lies within the interval, the range of plausible true effects includes no effect, so you can’t claim a real difference with confidence at that level. This situation can happen either because there truly is no effect or because the study has limited power—small sample size or high variability—that makes the estimate imprecise and widens the interval. It doesn’t prove the null hypothesis is true; it just means you cannot reject it with the current data. By contrast, a strong treatment effect would yield a interval that sits entirely on one side of the null, not crossing it, indicating statistical significance.