Step 1 :The null hypothesis for a true/false test is that the student is guessing, with a proportion of correct answers being \(0.50\).
Step 2 :A student gets \(4\) out of \(5\) questions correct and claims to know the material because the one-tailed p-value from the one-proportion z-test is \(0.090\), using a significance level of \(0.10\).
Step 3 :The one-proportion z-test requires a large enough sample size for the normal approximation to be valid, typically at least \(10\) successes and \(10\) failures.
Step 4 :With only \(5\) questions, the sample size is not large enough to expect at least \(10\) successes and \(10\) failures, indicating an issue with using the one-proportion z-test.
Step 5 :Calculating the correct p-value using the binomial distribution: successes = \(4\), trials = \(5\), \(p_{null} = 0.5\), resulting in a p-value of \(0.1875\).
Step 6 :The student's reported p-value of \(0.090\) and the suggested incorrect p-value of \(0.910\) are both different from the calculated p-value of \(0.1875\).
Step 7 :Conflating statistical significance with practical significance does not directly address the issue with the student's approach to the one-proportion z-test.
Step 8 :Using a confidence interval instead of a hypothesis test is not inherently wrong, but it does not address the issue with the sample size for the one-proportion z-test.
Step 9 :The correct answer is that the sample size is not large enough to use the one-proportion z-test, as indicated by option C.
Step 10 :The final answer is \(\boxed{C}\)