1. The problem involves interpreting the meaning of a 99% confidence level in hypothesis testing. 2. A 99% confidence level means that if we were to repeat the experiment many times, 99% of the confidence intervals calculated from those samples would contain the true population parameter. 3. The confidence level does not mean the probability that the specific interval contains the parameter; rather, it reflects the reliability of the estimation process. 4. The options likely describe different interpretations of the 99% confidence level; the correct interpretation is that we are 99% confident that the true parameter lies within the calculated interval based on the sample data. 5. The numbers 1100, 40, 1088.64, and 234.12 might be sample size, sample mean, or other statistics, but without further context, the key takeaway is understanding the confidence level concept. Final answer: A 99% confidence level means we are 99% confident that the true population parameter lies within the calculated confidence interval based on the sample data.