1 of20

When ap-value is high, this means there is strong evidence against the null hypothesis

	True
	False

Question

2 of20

In hypothesis testing, the hypothesis which is tentatively assumed to be true is called the

	correct hypothesis
	null hypothesis
	alternative hypothesis
	level of significance

Question

3 of20

If ap-value for a 2-sided test equals .065, thep-value for the 1-sided test using the same sample data will not be significant at the 1% level.

	True
	False

Question

4 of20

When a sample statistic is close to the hypothesized population parameter, thep-value for a significance test will typically be low.

	True
	False

Question

5 of20

Which of the following does not need to be known in order to compute theP-value?

	knowledge of whether the test is one-tailed or two-tailed
	the value of the test statistic
	the level of significance
	all of the above are needed

Question

6 of20

There is a high probability that James Bond can tell the difference between a shaken and a stirred martini.

	True
	False

Question

7 of20

Which of the following is the first step in hypothesis testing?

	It does not matter where you begin when you test hypotheses.
	Developing a null and alternative hypothesis.
	Drawing a sample from the population.
	Setting the cutoff value for rejecting the null hypothesis.

Question

8 of20

The critical value to be used in a statistical test is determined by the alpha level.

	True
	False

Question

9 of20

Many people describe hypothesis testing as counterintuitive because

	we test whether something happened in order to conclude that nothing happened.
	we test whether something happened but can still conclude that nothing happened.
	we can only conclude that nothing happened when we are 100% sure that something did not happen.
	we test whether nothing happened in order to conclude that something happened.

Question

10 of20

We always test a null hypothesis against an alternative.

	True
	False

Question

11 of20

The null hypothesis would be rejected if

	the test statistic is less than .05
	the test statistic is greater than .05
	the test statistic is in the rejection region
	the test statistic is more than two standard deviations from the mean

Question

12 of20

Hypotheses in a significance test are always stated in terms of the population parameters.

	True
	False

Question

13 of20

What is being addressed by a one-sample hypothesis test?

	Does our sample come from a particular population?
	Does our sample have the same variance as another population?
	Is our sample unusual in some way?
	Does the five-number summary of our sample match that of a particular population?

Question

14 of20

Let's suppose we conduct a hypothesis test about the mean value of something, and determine that we should reject the null hypothesis. What does that mean?

	Our hypothesized mean has been proven incorrect.
	The difference between our sample mean and our hypothesized mean was most likely due to random chance.
	The difference between our sample mean and our hypothesized mean was statistically significant.
	The difference between our sample mean and our hypothesized mean was not statistically significant.

Question

15 of20

Given a statistical test scenario with alpha set to .05 the absolute value of the critical value is determined to be 1.96. What does that also tell you about the hypothesis?

	It is likely that the null will not be rejected because the rejection region is too narrow.
	It is likely that a decision error will be made.
	The test is two-tailed.
	can't really say that it tells me anything

Question

16 of20

The formula is used to calculate

	the z-score of a data point.
	the z-score of a sample mean.
	the z-score of a sample proportion.
	none of the above

Question

17 of20

When testing a sample mean against a known population parameter thet-distribution is used when

	sis used as an estimate of
	x is used as an estimate of
	the sample size is small
	dfare unknown

Question

18 of20

t-tables typically show

	the area above thet-score
	the area below thet-score
	critical values oft
	z-score equivalents

Question

19 of20

A statistics professor announces to the class that a majority of students who take his class earn an A. A skeptical group of students suspects that this is too good to be true. If they were going to conduct a hypothesis test, what would the appropriate hypotheses be?

	Ho: 0.50, Ha: = 0.50
	Ho: 0.50, Ha: < 0.50
	Ho: < 0.50, Ha: > 0.50
	Ho: = 0.50, Ha: > 0.50

Question

20 of20

It is claimed that 66% of Boston residents have considered moving to a warmer climate. A group of city council members is hoping that the actual figure is lower than that, and wish to conduct a hypothesis test at the =.05 level of significance to determine if they are right. Which would be correct hypotheses for this test?

	Ho: 0.66, Ha: < 0.66
	Ho: 0.66, Ha: > 0.66
	Ho: < 0.66, Ha: = 0.66
	Ho: = 0.66, Ha: 0.66

1 of20

Voter registration records are compared to see if Yuma County has a higher proportion of registered voters than Pinal County. What is the appropriate statistical test?

	t-test for independent samples
	t-test for dependent samples
	z-test for proportions

Question

2 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

3 of20

In order to test whether there is a difference between two population means, an assumption is that

	we have two scores, or values, per subject
	1 2
	each value is sampled independently from each other value
	samples are dependent

Question

4 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 the 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

5 of20

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

	True
	False

Question

6 of20

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

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

Question

7 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

8 of20

When conducting a two-sample test of proportions, the difference between the population proportions

	is unknown
	is equal to p1- p2
	is assumed to be zero under the null hypothesis
	is unimportant

Question

9 of20

What does p (p-bar) represent?

	the weighted average of the sample proportions of successes
	the sample proportions of successes
	the population proportions of successes
	the number of successes in each sample

Question

10 of20

A _______ standard deviation in the population will result in a larger effect size.

	larger
	smaller
	more clearly defined
	less clearly defined

Question

11 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

12 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

13 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

14 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

15 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

16 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

17 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

18 of20

The power of a test is

	the probability of declaring a difference that does not actually exist
	the probability of finding a difference that does exist
	the probability of incorrectly rejecting Ho
	the probability of rejecting Ha when it is false

Question

19 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 is missing a real illness.

Question

20 of20

Two-tailed hypothesis tests have ________ power than one-tailed tests.

	slightly more
	slightly less
	more
	less