The statement 'If a 95% confidence interval for a treatment effect crosses zero, the result is not statistically significant' is:

The statement 'If a 95% confidence interval for a treatment effect crosses zero, the result is not statistically significant' is:

• A. True
• B. False
• C. It depends on the sample size
• D. It depends on the p-value

Answer: (A)

The main idea here is how a confidence interval relates to statistical significance. A 95% confidence interval for a treatment effect represents the range of values that are plausible for the true effect given the data, with zero meaning no effect. If zero lies inside that interval, zero is a plausible value for the true effect, so you cannot rule out no effect at the 5% level. In hypothesis-testing terms, the p-value would be greater than 0.05, so the result is not statistically significant. As a consequence, when the 95% CI crosses zero, you do not have evidence at the 0.05 level to claim a real treatment effect. If the interval did not include zero, you would have evidence of an effect at that level. The width of the interval—and thus whether it crosses zero—depends on sample size and variability: larger samples or less variability produce narrower intervals, making it easier to detect a true effect, while small samples can yield wide intervals that include zero even if a real effect exists.

The main idea here is how a confidence interval relates to statistical significance. A 95% confidence interval for a treatment effect represents the range of values that are plausible for the true effect given the data, with zero meaning no effect. If zero lies inside that interval, zero is a plausible value for the true effect, so you cannot rule out no effect at the 5% level. In hypothesis-testing terms, the p-value would be greater than 0.05, so the result is not statistically significant.

As a consequence, when the 95% CI crosses zero, you do not have evidence at the 0.05 level to claim a real treatment effect. If the interval did not include zero, you would have evidence of an effect at that level. The width of the interval—and thus whether it crosses zero—depends on sample size and variability: larger samples or less variability produce narrower intervals, making it easier to detect a true effect, while small samples can yield wide intervals that include zero even if a real effect exists.