A 95% confidence interval represents a range of values derived from a sample statistic, within which we expect the true population parameter (like the mean or proportion) to fall with 95% certainty. This does not mean that there is a 95% chance that the true parameter lies within this specific interval. Rather, if we were to take many samples and construct a confidence interval from each sample, approximately 95% of those intervals would contain the true population parameter.
If the confidence level is changed to 99%, the confidence interval would become wider. This increase in width occurs because a higher confidence level corresponds to a greater degree of certainty that the interval captures the true parameter. To achieve this increased certainty, the range of values must be expanded, which encompasses more potential values of the true parameter. Therefore, while a 99% confidence interval would provide a higher degree of confidence that the true parameter is within the range, it will also be less precise than a 95% confidence interval.