Comment on Chamch Post Confidence intervals provide a range of values within which the true population parameter is likely to fall. The width of the confidence interval is influenced by both the level of confidence and the sample size. 1. Confidence Level: o Higher confidence levels (e.g., 95%) result in wider intervals because they require more certainty, covering a broader range to account for possible variation. o Lower confidence levels (e.g., 90%) have narrower intervals as they allow for more uncertainty in exchange for precision. 2. Sample Size: o Larger sample sizes (e.g., 400) result in narrower confidence intervals due to the reduced standard error, which increases the precision of the estimate. o Smaller sample sizes (e.g., 100) produce wider confidence intervals because of higher variability in the estimates. Implications of Confidence Intervals The statement "Confidence intervals are underutilized" highlights a critical issue in data interpretation. Confidence intervals provide essential context about the reliability and precision of statistical estimates, which mere point estimates (e.g., sample means) lack. Using confidence intervals: Enhances the interpretation of data by showing the range of plausible values for a population parameter. It offers a transparent way to communicate the uncertainty inherent in statistical