(a) For comparing mean errors in proofreading (paper vs. computer screen) with assumed normal distribution and two groups, an independent samples t-test is appropriate.
(b) Null hypothesis: No difference in mean errors. Research hypothesis: A difference exists.
(c) Type I error: Incorrectly concluding a difference. Type II error: Failing to detect a true difference.
(d) Significance level (alpha): 0.05, indicating a 5% chance of Type I error in the t-test.
Sure, here are my answers to all parts:
(a) The choice of a statistical test depends on factors like data distribution and the number of groups. If assuming a normal distribution and two groups (paper vs. computer screen), the appropriate test would be an independent samples t-test, especially since a significance level of alpha = 0.05 suggests an assumption of normality in the data, as indicated in the provided information.
(b) The null hypothesis: The null hypothesis is the hypothesis that there is no difference between the mean number of errors detected by the two groups. In other words, the null hypothesis is that the method of proofreading (paper vs. computer screen) does not have any effect on the number of errors detected.
* The research hypothesis: The research hypothesis is the hypothesis that there is a difference between the mean number of errors detected by the two groups. In other words, the research hypothesis is that the method of proofreading (paper vs. computer screen) does have an effect on the number of errors detected.
(c) Type I error: A Type I error is an error that occurs when we reject the null hypothesis when it is actually true. In other words, a Type I error is when we conclude that there is a difference between the two groups when there really is no difference.
* Type II error: A Type II error is an error that occurs when we fail to reject the null hypothesis when it is actually false. In other words, a Type II error is when we conclude that there is no difference between the two groups when there really is a difference.
(d) The probability of making a Type I error is determined by the significance level of the test, which in this case is alpha = 0.05. This means that there is a 5% chance of making a Type I error if we use an independent samples t-test to analyze the data from this experiment.