How should a confidence interval be interpreted?

• A. A confidence interval provides a range in which the true population parameter is likely to fall with a given level of confidence.
• B. A confidence interval identifies the exact value of the population parameter.
• C. A confidence interval measures the variability of the sample mean only.
• D. A confidence interval determines whether results are statistically significant.

Answer: (A)

A confidence interval represents a range of values that, based on your sample data, is plausible for the true population parameter. When you choose a confidence level, such as 95%, you are committing to a method that would produce intervals that contain the true parameter in about 95% of repeated samples. This reflects how precise (or imprecise) your estimate is: a narrower interval suggests more precision, a wider interval suggests less. It does not promise the exact value of the population parameter, and it isn’t about measuring only the variability of the sample mean, nor does it by itself determine statistical significance.