What term refers to the percentage of confidence intervals that contain the true parameter?

The correct term for the percentage of confidence intervals that contain the true parameter is the confidence level. This concept is fundamental in statistics, as it reflects how certain we are that a particular calculated interval truly covers the unknown population parameter.

For example, if a confidence level is set at 95%, it means that if we were to take 100 different samples and calculate a confidence interval from each of those samples, we would expect about 95 of those intervals to contain the true parameter. This illustrates the reliability and consistency of the method used to generate the confidence intervals.

In contrast, the significance level pertains to hypothesis testing and represents the probability of rejecting the null hypothesis when it is actually true; this does not relate directly to how many intervals contain the true parameter. The error margin, or margin of error, indicates the range of values above and below the sample estimate, but it does not reflect the coverage percentage of intervals. The probability coefficient is a more general term that doesn't specifically pertain to confidence intervals and can refer to various aspects of probability in statistics.