1 of20

To compare the means of samples of two related populations, the dependency between the populations must be taken into account by calculating the differences between matched sets of values in the two samples.

	True
	False

Question

2 of20

When testing for differences between the means of two related populations, what is the null hypothesis?

	The difference between the population means is equal to 0.
	The difference between the population means is equal to 1.
	The difference between the population means is greater than 1.
	The difference between the population means is greater than 0.

Question

3 of20

A Type I error occurs when we

	correctly fail to reject a false null hypothesis.
	correctly reject a false null hypothesis.
	incorrectly reject a false null hypothesis.
	incorrectly reject a true null hypothesis.

Question

4 of20

A calculated value of d = .45 indicates

	a small effect size
	a medium effect size
	a large effect size
	a significant effect size

Question

5 of20

If we are conducting a hypothesis test with the following hypotheses: Ho: =25, Ha: 25, which of the following would represent a Type I Error?

	We fail to reject the null hypothesis when, in fact, is not 25.
	We reject the null hypothesis when, in fact, is not 25.
	We fail to reject the null hypothesis when, in fact, is actually 25.
	We reject the null hypothesis when, in fact, is actually 25.

Question

6 of20

A statistician wishes to determine the difference between two population means. A sample of 10 items from Population #1 yields a mean of 185 with a standard deviation of 20. The sample of 12 items from Population #2 yields a mean of 200 with a standard deviation of 25. Assume that the values are normally distributed in each population. How many degrees of freedom are there for this test?

	11
	20
	22
	21

Question

7 of20

When the null hypothesis has been true, but the sample information has resulted in the rejection of the null, a _________ has been made.

	level of significance
	Type II error
	critical value
	Type I error

Question

8 of20

As sample size is increased

	the likelihood of a statistically significant result increases.
	the likelihood of a statistically significant result decreases.
	effect size determination becomes irrelevant.

Question

9 of20

In testing a hypothesis about two population means, if the t-distribution is used which of the following assumptions is required?

	Both populations are normally distributed.
	The sample sizes are equal.
	Both population means are the same.
	The standard deviations are not the same.

Question

10 of20

When comparing population proportions, what is the null hypothesis if the alternative hypothesis is₁ >₂?

	Ho: 1 < 2
	Ho: 12
	Ho: 1 = 2
	Ho: 1 2

Question

11 of20

In hypothesis testing, beta is

	the probability of committing a Type II error
	the probability of committing a Type I error
	the probability of either a Type I or Type II, depending on the hypothesis to be tested
	none of the above

Question

12 of20

With respect to effect size, what is the issue with reporting only significance levels of hypothesis tests?

	There could be a decision error.
	The outcome could be due to chance.
	It may be misleading if the effect is too small to be of any practical significance.

Question

13 of20

The maximum probability of a Type I error that the decision maker will tolerate is called the

	level of significance
	critical value
	decision value
	probability value

Question

14 of20

The statistical test for comparing the means of two matched samples uses the normal distributions.

	True
	False

Question

15 of20

Why do we use the t-distribution instead of the normal distribution as our reference distribution?

	The population variances are unknown and we are estimating them from a sample.
	You never use the normal distribution in applied statistics.
	Because our sample size is large.
	Since we are using the standard deviation instead of the variances in our calculations.

Question

16 of20

A Type II error occurs when

	we correctly fail to reject a false null hypothesis.
	we incorrectly fail to reject a false null hypothesis.
	we incorrectly reject a true null hypothesis.
	we correctly reject a false null hypothesis.

Question

17 of20

If "going to the doctor" is used as an analogy, then power is

	your doctor confirming that you really are sick.
	getting scared for nothing.
	your doctor stating you are not sick when there is nothing wrong.
	your doctor missing a real illness.

Question

18 of20

If you test for the difference between the means of twoindependent samples, there are how many degrees of freedom?

	(n1 + n2) / 2 - 1
	n1 + n2 - 2
	(n1 + n2) / 2
	n -1

Question

19 of20

Is there a statistically significant difference (p < .05) between the average wait times at these two doctor's offices? Below are 10 wait times in minutes.

Dr. Strangelove : 12 17 21 22 29 18 13 25 20 27

Dr. Zhivago 23 24 27 27 26 26 31 29 23 23

	The test statistic of 2.78 is greater than .05 so we conclude the difference is statistically significant.
	You can't use the 2-sample t-test unless than sample size is greater than 30.
	The difference in wait times of 5.5 minutes is *NOT* statistically significant because the p-value of .0156 is less than .05.
	The difference in wait times of 5.5 minutes is statistically significant because the p-value of .0156 is less than .05.

Question

20 of20

When you subtract the differences between two means repeatedly, then graph the differences, what pattern emerges?

	A normal bell-shaped curve
	It depends on the data
	A rectangular distribution
	A horizontal line