In statistical terms, what does a higher confidence level generally indicate about the interval estimate?

Selecting a higher confidence level signifies that you are aiming for a greater assurance that the interval estimate will contain the true population parameter. To achieve this heightened confidence, the interval must become wider, as it needs to account for a greater range of possible values where the true parameter may lie.

For example, if you move from a 95% confidence level to a 99% confidence level, the corresponding confidence interval will expand. This is done to encompass more variability within the data, thus improving the likelihood that the interval captures the true parameter. As a result, while you gain certainty about including the true value, the interval becomes broader, indicating a less precise estimate, although statistically more reliable.

In contrast, a tighter estimate (which would mean a smaller interval) would correspond to a lower confidence level, where you trade off some certainty for precision. Therefore, the correct answer accurately reflects the relationship between confidence levels and the width of interval estimates.