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?
9. Which of these is an example of a test statistic (i.e. not a parameter)?
10. What effect will choosing a higher alpha (greater that the standard .05) have on your error rate?
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)
Get Answers For Free
Most questions answered within 1 hours.