Final answer:
To calculate the interval where 95 percent of sample means fall given a population mean and standard deviation, we'd typically use the standard error of the mean. For a 95 percent confidence interval without a sample size, about 95 percent of sample means are expected to be within two standard deviations of the population mean. Accordingly, the interval would be approximately (0.9309, 0.9389).
Step-by-step explanation:
When determining the interval within which 95 percent of sample means will fall given a population with true mean μ = 0.9349 and standard deviation σ = 0.0020, we apply the principles of the Central Limit Theorem and the concept of confidence intervals. A 95 percent confidence interval suggests that we are 95 percent confident that the true mean is within a certain range of values around the sample mean.
To calculate this interval, we typically use the standard error of the mean (SEM), which is the standard deviation divided by the square root of the sample size (n). However, the question does not provide us with a sample size. If we had the sample size, the formula would be:
μ ± (Z*σ/√n)
Where Z is the Z-score corresponding to the desired confidence level (1.96 for 95% confidence level). Without the sample size, we cannot compute the precise interval. But generally, using the empirical rule, we can say that 95 percent of sample means are expected to be within two standard deviations of the true mean.
Therefore, the interval approximately would be:
(0.9349 ± 2*0.0020), which simplifies to (0.9309, 0.9389).