Which term describes a range of values derived from sample data that is used to estimate a population parameter with a certain level of confidence?

Which term describes a range of values derived from sample data that is used to estimate a population parameter with a certain level of confidence?

• A. Hypothesis testing
• B. P-Value interval
• C. Prediction interval
• D. Confidence interval

Answer: (D)

Confidence interval is a range of values derived from sample data that is used to estimate a population parameter with a stated level of confidence. It combines the observed sample statistic (like the sample mean) with a margin of error that reflects sampling variability and the chosen confidence level. If you repeated the study many times and built intervals at the same level, about that proportion of them would contain the true parameter. The width of the interval depends on the variability in the data and the sample size: larger samples reduce the standard error and produce a narrower interval, while more variability widens it. The chosen confidence level (e.g., 95%) determines how often the method would capture the true parameter in repeated samples; higher confidence yields a wider interval. This differs from predicting where a future individual observation will fall (that uses a prediction interval) and from hypothesis testing, which focuses on making a decision about a claim rather than estimating a range for the parameter.

Confidence interval is a range of values derived from sample data that is used to estimate a population parameter with a stated level of confidence. It combines the observed sample statistic (like the sample mean) with a margin of error that reflects sampling variability and the chosen confidence level. If you repeated the study many times and built intervals at the same level, about that proportion of them would contain the true parameter.

The width of the interval depends on the variability in the data and the sample size: larger samples reduce the standard error and produce a narrower interval, while more variability widens it. The chosen confidence level (e.g., 95%) determines how often the method would capture the true parameter in repeated samples; higher confidence yields a wider interval.

This differs from predicting where a future individual observation will fall (that uses a prediction interval) and from hypothesis testing, which focuses on making a decision about a claim rather than estimating a range for the parameter.