Final answer:
The correct answer is option A. True, since a confidence interval is not guaranteed to contain the true parameter value in every single instance, though it is designed to do so a specified percentage of the time.
Step-by-step explanation:
The correct answer is option A. An interval estimate, such as a confidence interval, is constructed to estimate the true value of a population parameter based on sample data. While confidence intervals are designed to include the true parameter value a certain percentage of the time, they are not guaranteed to contain the true value in every single instance. This uncertainty arises because each sample may differ due to natural variability, and the interval is based on the data from just one sample. For example, if we create a 95% confidence interval, we expect that if we were to take many samples and create a confidence interval from each one, approximately 95% of those intervals would contain the true population mean.
Therefore, there is always a chance, albeit small, that a given confidence interval might not contain the true parameter value. This is particularly true with smaller sample sizes, which result in wider intervals due to increased variability. Also, the width of the confidence interval is affected by the desired level of confidence; a higher confidence level like 99% would result in a wider interval than a 90% confidence level.