Question

# 8. In the population, there is no difference between men and women on a certain test....

8. In the population, there is no difference between men and women on a certain test.

However, the mean for men was higher than the mean for women in your sample, and with a t of 4.532 and a p-value of .004, you have determined it is appropriate to reject the null hypothesis and conclude a difference does exist.

What type of error did you make?

• A. Type I
• B. Type II
• C. You confirmed the null hypothesis.
• D. Standard error

9. Which of these is an example of a test statistic (i.e. not a parameter)?

• A. The population mean
• B. The sample mean
• C. The null and alternate hypotheses
• D. Type II

10. What effect will choosing a higher alpha (greater that the standard .05) have on your error rate?

• A. More difficulty in finding genuine effects.
• B. A decrease in the probability of type I error.
• C. A decrease in the probability of type I error and an increase in the probability of type II.
• D. An increase in the probability of the type I error.

8. The probability of rejecting the null hypothesis when it is true is called type-I error.

Here in the problem in the population there is no difference between men and women in a certain test. But in the the conclusion of the test we reject the null hypothesis so I made type-I error (A) in the following testing.

9. Statistic is actually a function of sample values.

Here sample mean is the function of sample values so sample mean is a statistic (B).

10. Level of significance is denoted as alpha is actually upper limit of type-I error. So if we increase the value of alpha actually we allow more type-I error in the testing procedure.

So, an increase in the probability of the type-I error (D)

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