Final answer:
The statement is false. Confidence intervals estimate where a true score lies within a certain confidence level; they do not assume the observed score is the true score.
Step-by-step explanation:
The statement is false. When we calculate a confidence interval of an individual's test score, we don't assume that their observed score is their true score. Instead, we are estimating the range within which the true score is likely to fall given a certain level of confidence. The observed score is just one of the possible samples from the population of all possible test scores. The calculated confidence interval takes into account the variability of scores in the sample to estimate the true score with a specified level of confidence.
For clarity, if we constructed 100 confidence intervals at the 90% confidence level, we would expect that approximately 90 of them would contain the true population mean exam score. Similarly, at a 95% confidence level, we would anticipate that approximately 95 of the 100 intervals would contain the true population mean.
Sample mean and population mean are different; the confidence interval helps us estimate the population mean based on our sample.