Final answer:
A 95% confidence interval is narrower than a 99% confidence interval, as the latter includes a larger portion of the distribution. As the confidence level increases, so does the width of the interval, reflecting greater certainty that it contains the true population parameter.
Step-by-step explanation:
When comparing a 95% confidence interval to a 99% confidence interval, the primary difference lies in the range that the intervals cover in reference to the population parameter. A 95% confidence interval is narrower because it captures less of the distribution (95%) which means there's a 5% chance the true parameter is outside this range.
On the other hand, a 99% confidence interval is wider because it seeks to include more of the distribution (99%). Consequently, there's only a 1% chance that the true parameter falls outside this interval. If you calculate confidence intervals repeatedly using the same sampling method, more of the intervals will contain the population mean if you're using a higher confidence level (99%) compared to a lower one (95%).
As the confidence level decreases from 99% to 90%, the confidence interval would become even narrower. This is because a lower confidence level includes a smaller part of the distribution, reflecting less certainty about the interval containing the true population parameter.