Step 1 :Define the null and alternative hypotheses. The null hypothesis \(H_{0}\) is that the means of the proctored and nonproctored tests are equal, i.e., \(\mu_{1} = \mu_{2}\). The alternative hypothesis \(H_{1}\) is that the mean of the nonproctored tests is greater than the mean of the proctored tests, i.e., \(\mu_{1} < \mu_{2}\).
Step 2 :Calculate the test statistic and the P-value. The test statistic is -1.79 and the P-value is 0.042.
Step 3 :Compare the P-value with the significance level. The P-value (0.042) is less than the significance level (0.05), so we reject the null hypothesis \(H_{0}\). There is sufficient evidence to support the claim that students taking nonproctored tests get a higher mean score than those taking proctored tests.
Step 4 :Construct a confidence interval for the difference in means. The confidence interval is approximately (-0.98, 14.82). This interval does not contain zero, which suggests that there is a significant difference in the means.
Step 5 :The final answer is: The null and alternative hypotheses are: \(H_{0}: \mu_{1}=\mu_{2}\) and \(H_{1}: \mu_{1}<\mu_{2}\). The conclusion of the test is: Reject \(H_{0}\). There is sufficient evidence to support the claim that students taking nonproctored tests get a higher mean score than those taking proctored tests. The confidence interval for the difference in means is approximately \(\boxed{(-0.98, 14.82)}\).