Final answer:
The correctness of the provided confidence interval (67%, 74%) cannot be validated without additional information. Confidence intervals are calculated using sample data, and their widths depend on the level of confidence desired. Sampling variability can cause slight discrepancies in results from different samples.
Step-by-step explanation:
The statement that a 95% confidence interval for the proportion is (67%, 74%) is false without additional information, such as the sample proportion or the number of newborns requiring special care. A 95% confidence interval means that if we took repeated samples, approximately 95% of the confidence intervals calculated from those samples would contain the true value of the population proportion.
Concerning the sampled newborn babies, to construct a confidence interval, one must calculate it using the sample proportion, the sample size, and the desired confidence level. The provided interval only states the range, without reference to how it was obtained, so we cannot confirm its accuracy.
Sampling variability is a critical concept and explains why different researchers might find slightly different proportions in their results due to random fluctuations inherent in the sampling process.
If we decrease the confidence level, for example from 99% to 90%, the confidence interval generally becomes narrower, as it reflects a reduced level of certainty around the estimated population parameter.