Final answer:
The standard error of the mean (SEM) indicates the variability of the sample mean from the true mean, which can't be calculated here without additional data. For a 95% confidence interval, we need the sample mean, SEM, and critical t value, which is constructed as the sample mean ± (t value * SEM).
Step-by-step explanation:
The standard error of the mean (SEM) explains how much the sample mean would vary from the true population mean. It is calculated by dividing the population standard deviation by the square root of the sample size.
To create a 95% confidence interval for the freshmen's mean test score, one would generally need the sample mean, the standard error of the mean, and the critical value from the t-distribution (since population standard deviation is often unknown in practice).
The confidence interval is calculated as the sample mean plus or minus the product of the critical t value and the SEM. This interval estimates the range within which the true population mean is likely to lie with 95% certainty.