Testing hypothesis Essay

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Testing hypothesis

Introduction to
Hypothesis
Testing
8.1

Inferential Statistics
and Hypothesis Testing

LEARNING OBJECTIVES

8.2 Four Steps to
Hypothesis Testing

After reading this chapter, you should be able to:

8.3

Hypothesis Testing and
Sampling Distributions

8.4

Making a Decision:
Types of Error

8.5

Testing a Research
Hypothesis: Examples
Using the z Test

8.6

Research in Focus:
Directional Versus
Nondirectional Tests

8.7

Measuring the Size of
an Effect: Cohen’s d

8.8

Effect Size, Power, and
Sample Size

8.9

Additional Factors That
Increase Power

1 Identify the four steps of hypothesis testing.
2 Define null hypothesis, alternative hypothesis,

level of significance, test statistic, p value, and
statistical significance.

3 Define Type I error and Type II error, and identify the
type of error that researchers control.

4 Calculate the one-independent sample z test and
interpret the results.

5 Distinguish between a one-tailed and two-tailed test,
and explain why a Type III error is possible only with
one-tailed tests.

6 Explain what effect size measures and compute a
Cohen’s d for the one-independent sample z test.

7 Define power and identify six factors that influence power. 8 Summarize the results of a one-independent sample z test in American Psychological Association (APA)
format.

8.10 SPSS in Focus:
A Preview for
Chapters 9 to 18
8.11 APA in Focus:
Reporting the Test
Statistic and Effect Size

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PART III: PROBABILITY AND THE FOUNDATIONS OF INFERENTIAL STATISTICS

8.1 INFERENTIAL STATISTICS AND HYPOTHESIS TESTING
We use inferential statistics because it allows us to measure behavior in samples to learn more about the behavior in populations that are often too large or inaccessi­ ble. We use samples because we know how they are related to populations.

For example, suppose the average score on a standardized exam in a given population is 1,000. In Chapter 7, we showed that the sample mean as an unbiased estimator of the population mean—if we selected a random sample from a population, then on average the value of the sample mean will equal the population mean. In our exam­ ple, if we select a random sample from this population with a mean of 1,000, then on average, the value of a sample mean will equal 1,000.

On the basis of the central limit theorem, we know that the probability of selecting any other sample mean value from this population is normally distributed.

In behavioral research, we select samples to learn more about populations of interest to us. In terms of the mean, we measure a sample mean to learn more about the mean in a population. Therefore, we will use the sample mean to describe the population mean. We begin by stating the value of a population mean, and then we select a sample and measure the mean in that sample.

On average, the value of the sample mean will equal the population mean. The larger the difference or discrep­ ancy between the sample mean and population mean, the less likely it is that we could have selected that sample mean, if the value of the population mean is cor­ rect. This type of experimental situation, using the example of standardized exam scores, is illustrated in Figure 8.1.

FIGURE 8.1
The sampling distribution for a
population mean is equal to 1,000.
If 1,000 is the correct population
mean, then we know that, on
average, the sample mean will
equal 1,000 (the population mean).
Using the empirical rule, we know
that about 95% of all samples
selected from this population will
have a sample mean that falls
within two standard deviations
(SD) of the mean. It is therefore
unlikely (less than a 5%
probability) that we will measure a
sample mean beyond
2 SD from the population mean, if
the population mean is indeed
correct.

We expect the
sample mean to be
equal to the
population mean.

µ = 1000

The method in which we select samples to learn more about characteristics in a given population is called hypothesis testing. Hypothesis testing is really a systematic way to test claims or ideas about a group or population. To illustrate,

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3

suppose we read an article stating that children in the United States watch an aver­ age of 3 hours of TV per week. To test whether this claim is true, we record the time (in hours) that a group of 20 American children (the sample), among all children in the United States (the population), watch TV. The mean we measure for these 20 children is a sample mean. We can then compare the sample mean we select to the population mean stated in the article.

Hypothesis testing or significance testing is a method for testing a claim or hypothesis about a parameter in a population, using data measured in a sample. In this method, we test some hypothesis by determining the likelihood that a sample statistic could have been selected, if the hypothesis regarding the population parameter were true.

DEFINITION

The method of hypothesis testing can be summarized in four steps. We will describe each of these four steps in greater detail in Section

8.2.
1. To begin, we identify a hypothesis or claim that we feel should be tested. For example, we might want to test the claim that the mean number of hours that children in the United States watch TV is 3 hours.

2. We select a criterion upon which we decide that the claim being tested is true or not. For example, the claim is that children watch 3 hours of TV per week. Most samples we select should have a mean close to or equal to 3 hours if the claim we are testing is true. So at what point do we decide that the discrepancy between the sample mean and 3 is so big that the claim we are testing is likely not true? We answer this question in this step of hypothesis testing.

3. Select a random sample from the population and measure the sample mean. For example, we could select 20 children and measure the mean time (in hours) that they watch TV per week.

4. Compare what we observe in the sample to what we expect to observe if the claim we are testing is true. We expect the sample mean to be around 3 hours. If the discrepancy between the sample mean and population mean is small, then we will likely decide that the claim we are testing is indeed true. If the discrepancy is too large, then we will likely decide to reject the claim as being not true.

1. On average, what do we expect the sample mean to be equal to? 2. True or false: Researchers select a sample from a population to learn more about characteristics in that sample.

NOTE: Hypothesis testing is
the method of testing whether
claims or hypotheses regarding
a population are likely to be
true.

LE A R N I N G
C H EC K 1

characteristics in the population that the sample was selected from. Answers: 1. The population mean; 2. False. Researchers select a sample from a
population to learn more about

4

PART III: PROBABILITY AND THE FOUNDATIONS OF INFERENTIAL STATISTICS

8.2 FOUR STEPS TO HYPOTHESIS TESTING
The goal of hypothesis testing is to determine the likelihood that a population parameter, such as the mean, is likely to be true. In this section, we describe the four steps of hypothesis testing that were briefly introduced in Section 8.1:

Step 1: State the hypotheses.

Step 2: Set the criteria for a decision.

Step 3: Compute the test statistic.

Step 4: Make a decision.

Step 1: State the hypotheses. We begin by stating the value of a population mean in a null hypothesis, which we presume is true. For the children watching TV example, we state the null hypothesis that children in the United States watch an average of 3 hours of TV per week. This is a starting point so that we can decide whether this is likely to be true, similar to the presumption of innocence in a courtroom. When a defendant is on trial, the jury starts by assuming that the defendant is innocent.

The basis of the decision is to determine whether this assumption is true. Likewise, in hypothesis testing, we start by assuming that the hypothesis or claim we are testing is true. This is stated in the null hypothesis. The basis of the decision is to determine whether this assumption is likely to be true.

DEFINITION

NOTE: In hypothesis testing,
we conduct a study to test
whether the null hypothesis is
likely to be true.

DEFINITION

The null hypothesis (H0), stated as the null, is a statement about a population parameter, such as the population mean, that is assumed to be true. The null hypothesis is a starting point. We will test whether the value stated in the null hypothesis is likely to be true.

Keep in mind that the only reason we are testing the null hypothesis is because we think it is wrong. We state what we think is wrong about the null hypothesis in an alternative hypothesis. For the children watching TV example, we may have reason to believe that children watch more than (>) or less than ( .05), we retain the null hypothesis.

The decision to reject or retain the null hypothesis is called significance. When the p value is less than .05, we reach significance; the decision is to reject the null hypothesis. When the p value is greater than .05, we fail to reach significance; the decision is to retain the null hypothesis. Figure 8.3 shows the four steps of hypothesis testing.

NOTE: Researchers make
decisions regarding the null
hypothesis. The decision can
be to retain the null (p > .05)
or reject the null (p < .05).

LE A R N I N G
C H EC K 2

1. State the four steps of hypothesis testing.
2. The decision in hypothesis testing is to retain or reject which hypothesis: the null or alternative hypothesis?
3. The criterion or level of significance in behavioral research is
typically set at what probability value?
4. A test statistic is associated with a p value less than .05 or 5%. What is the deci­ sion for this hypothesis test?
5. If the null hypothesis is rejected, then did we reach significance?

7

outcome; 4. Reject the null; 5. Yes.
Step 3: Compute the test statistic. Step 4: Make a decision; 2. Null; 3. A .05 or 5% likelihood for obtaining a sample Answers: 1. Step 1: State the null and alternative hypothesis. Step 2: Determine the level of significance.

8

PART III: PROBABILITY AND THE FOUNDATIONS OF INFERENTIAL STATISTICS

STEP 1: State the hypotheses.
A researcher states a null
hypothesis about a value in the
population (H0) and an
alternative hypothesis that
contradicts the null hypothesis.
STEP 2: Set the criteria for a
decision. A criterion is set upon
which a researcher will decide
whether to retain or reject the
value stated in the null
hypothesis.

POPULATION

————————————————-Level of Significance (Criterion) ————————————————–

A sample is selected from the
population, and a sample mean
is measured.

Conduct a study
with a sample
selected from a
population.

STEP 3: Compute the test
statistic. This will produce a
value that can be compared to
the criterion that was set before
the sample was selected.

Measure data
and compute
a test statistic.

STEP 4: Make a decision.
If the probability of obtaining a
sample mean is less than 5%
when the null is true, then reject
the null hypothesis.
If the probability of obtaining a
sample mean is greater than 5%
when the null is true, then
retain the null hypothesis.

FIGURE 8.3
A summary of hypothesis testing.

8.3
HYPOTHESIS TESTING AND
SAMPLING DISTRIBUTIONS
The logic of hypothesis testing is rooted in an understanding of the sampling
distribution of the mean. In Chapter 7, we showed three characteristics of the mean, two of which are particularly relevant in this section:

1. The sample mean is an unbiased estimator of the population mean. On average, a randomly selected sample will have a mean equal to that in the population. In hypothesis testing, we begin by stating the null hypothesis. We expect that, if the null hypothesis is true, then a random sample selected from a given population will have a sample mean equal to the value stated in the null hypothesis.

2. Regardless of the distribution in the population, the sampling distribution of the sample mean is normally distributed. Hence, the probabilities of all other possible sample means we could select are normally distributed.

Using this distribution, we can therefore state an alternative hypothesis to locate the probability of obtaining sample means with less than a 5% chance of being selected if the value stated in the null hypothesis is true. Figure 8.2 shows that we can identify sample mean outcomes in one or both tails.

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To locate the probability of obtaining a sample mean in a sampling distribution, we must know (1) the population mean and (2) the standard error of the mean (SEM; introduced in Chapter 7).

Each value is entered in the test statistic formula computed in Step 3, thereby allowing us to make a decision in Step 4. To review, Table 8.1 displays the notations used to describe populations, samples, and sampling distributions. Table 8.2 summarizes the characteristics of each type of distribution. TABLE 8.1  A review of the notation used for the mean, variance, and standard deviation in population, sample, and sampling distributions.

Characteristic

Population

Mean

m

Sample

M or X

Variance

s2

s2 or SD 2

Standard
deviation

s

Sampling Distribution

s or SD

mM = m
2
σM =

σM =

σ2
n
σ
n

TABLE 8.2  A review of the key differences between population, sample, and sampling distributions.

Population Distribution

Sample Distribution

Distribution of Sample Means

What is it?

Scores of all persons in a
population

Scores of a select
portion of persons from
the population

All possible sample means that
can be drawn, given a certain
sample size

Is it accessible?

Typically, no

Yes

Yes

What is the shape?

Could be any shape

Could be any shape

Normally distributed

1. For the following statement, write increases or decreases as an answer. The like­ lihood that we reject the null hypothesis (increases or decreases): a. The closer the value of a sample mean is to the value stated by the null hypothesis?

b. The further the value of a sample mean is from the value stated in the null hypothesis?
2. A researcher selects a sample of 49 students to test the null hypothesis that the average student exercises 90 minutes per week. What is the mean for the sam­ pling distribution for this population of interest if the null hypothesis is true?

LE A R N I N G
C H EC K 3

9

Answers: 1. (a) Decreases, (b) Increases; 2. 90 minutes.

10

PART III: PROBABILITY AND THE FOUNDATIONS OF INFERENTIAL STATISTICS

8.4 MAKING A DECISION: TYPES OF ERROR
In Step 4, we decide whether to retain or reject the null hypothesis. Because we are observing a sample and not an entire population, it is possible that a conclusion may be wrong. Table 8.3 shows that there are four decision alternatives regarding the truth and falsity of the decision we make about a null hypothesis:

1. The decision to retain the null hypothesis could be correct.
2. The decision to retain the null hypothesis could be incorrect.
3. The decision to reject the null hypothesis could be correct.
4. The decision to reject the null hypothesis could be incorrect. TABLE 8.3  Four outcomes for making a decision. The decision can be either correct (correctly reject or retain null) or wrong (incorrectly reject or retain null).

Decision
Retain the null
True
Truth in the
population

False

Reject the null

CORRECT
1–a

TYPE I ERROR
a

TYPE II ERROR
b

CORRECT
1–b
POWER

We investigate each decision alternative in this section. Since we will observe a sample, and not a population, it is impossible to know for sure the truth in the population. So for the sake of illustration, we will assume we know this. This assumption is labeled as truth in the population in Table 8.3. In this section, we will introduce each decision alternative.

DECISION: RETAIN THE NULL HYPOTHESIS
When we decide to retain the null hypothesis, we can be correct or incorrect. The correct decision is to retain a true null hypothesis. This decision is called a null result or null finding. This is usually an uninteresting decision because the deci­ sion is to retain what we already assumed: that the value stated in the null hypoth­ esis is correct. For this reason, null results alone are rarely published in behavioral research.

The incorrect decision is to retain a false null hypothesis. This decision is an example of a Type II error, or b error. With each test we make, there is always some probability that the decision could be a Type II error. In this decision, we decide to retain previous notions of truth that are in fact false. While it’s an error, we still did nothing; we retained the null hypothesis. We can always go back and conduct more studies.

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Type II error, or beta (b) error, is the probability of retaining a null hypothesis that is actually false.

DECISION: REJECT THE NULL HYPOTHESIS
When we decide to reject the null hypothesis, we can be correct or incorrect. The incorrect decision is to reject a true null hypothesis. This decision is an example of a Type I error. With each test we make, there is always some probability that our decision is a Type I error. A researcher who makes this error decides to reject previ­ ous notions of truth that are in fact true.

Making this type of error is analogous to finding an innocent person guilty. To minimize this error, we assume a defendant is innocent when beginning a trial. Similarly, to minimize making a Type I error, we assume the null hypothesis is true when beginning a hypothesis test. Type I error is the probability of rejecting a null hypothesis that is actually true. Researchers directly control for the probability of committing this type of
error.

11

DEFINITION

NOTE: A Type II error, or beta
(b) error, is the probability of
incorrectly retaining the null
hypothesis.

DEFINITION

An alpha (a) level is the level of significance or criterion for a hypothesis test. It is the largest probability of committing a Type I error that we will allow and still decide to reject the null hypothesis.

Since we assume the null hypothesis is true, we control for Type I error by stating a level of significance. The level we set, called the alpha level (symbolized as a), is the larg­ est probability of committing a Type I error that we will allow and still decide to reject the null hypothesis.

This criterion is usually set at .05 (a = .05), and we compare the alpha level to the p value. When the probability of a Type I error is less than 5% (p < .05), we decide to reject the null hypothesis; otherwise, we retain the null hypothesis. The correct decision is to reject a false null hypothesis.

There is always some probability that we decide that the null hypothesis is false when it is indeed false. This decision is called the power of the decision-making process. It is called power because it is the decision we aim for. Remember that we are only testing the null hypothesis because we think it is wrong. Deciding to reject a false null hypothesis, then, is the power, inasmuch as we learn the most about populations when we accurately reject false notions of truth.

This decision is the most published result in behavioral research. The power in hypothesis testing is the probability of rejecting a false null hypothesis. Specifically, it is the probability that a randomly selected sample will show that the null hypothesis is false when
the null hypothesis is indeed false.

NOTE: Researchers directly
control for the probability of
a Type I error by stating an
alpha (a) level.

NOTE: The power in hypothesis
testing is the probability of
correctly rejecting the value
stated in the null hypothesis.

DEFINITION

LE A R N I N G
C H EC K 4

1. What type of error do we directly control?
2. What type of error is associated with decisions to retain the null? 3. What type of error is associated with decisions to reject the null? 4. State the two correct decisions that a researcher can make. hypothesis.

Answers: 1. Type I error; 2. Type II error; 3. Type I error; 4. Retain a true null hypothesis and reject a false null

12

PART III: PROBABILITY AND THE FOUNDATIONS OF INFERENTIAL STATISTICS

8.5
TESTING A RESEARCH HYPOTHESIS:
EXAMPLES USING THE Z TEST
The test statistic in Step 3 converts the sampling distribution we observe into a standard normal distribution, thereby allowing us to make a decision in Step 4.

The test statistic we use depends largely on what we know about the population. When we know the mean and standard deviation in a single population, we can use the one–independent sample z test, which we will use in this section to illustrate the four steps of hypothesis testing.

DEFINITION

NOTE: The z test is used to
test hypotheses about a
population mean when the
population variance is known.

The one–independent sample z test is a statistical procedure used to test hypotheses concerning the mean in a single population with a known variance. Recall that we can state one of three alternative hypotheses: A population mean is greater than (>), less than ()

NOTE: An upper-tail critical
test is conducted when it is
not possible or highly unlikely
that a sample mean will fall
below the population mean
stated in the null hypothesis.

DEFINITION

E X A M PL E 8 . 2

In Example 8.2, we will use the z test for a directional, or one-tailed test, where the alternative hypothesis is stated as greater than (>) the null hypothesis. A direc­ tional test can also be stated as less than () or less than () this value: H0: m = 558

Mean test scores are equal to 558 in the population of students at the elite school.
H1: m > 558
Mean test scores are greater than 558 in the population of
students at the elite school.
Step 2: Set the criteria for a decision. The level of significance is .05, which makes the alpha level a = .05. To determine the critical value for an upper-tail critical test, we locate the probability .0500 toward the tail in column C in the unit normal table.

The z-score associated with this probability is between z = 1.64 and z = 1.65. The average of these z-scores is z = 1.645. This is the critical value or cutoff for the rejection region. Figure 8.6 shows that for this test, we place all the value of alpha in the upper tail of the standard normal distribution.

NOTE: For one-tailed tests, the
alpha level is placed in a single
tail of a distribution. For
upper-tail critical tests, the
alpha level is placed above the
mean in the upper tail.

Step 3: Compute the test statistic. Step 2 sets the stage for making a decision because the criterion is set. The probability is less than 5% that we will obtain a sample mean that is at least 1.645 standard deviations above the value of the population mean stated in the null hypothesis. In this step, we will compute a test statistic to determine whether or not the sample mean we selected is beyond the critical value we stated in Step 2.

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17

Critical value for an uppertail critical test with α = .05

Rejection region
α = .05

−3

−2

−1

0

Null

1

2

3

z = 1.645

FIGURE 8.6
The critical value (1.645) for a
directional (upper-tail critical)
hypothesis test at a .05 level of
significance. When the test
statistic exceeds 1.645, we reject
the null hypothesis; otherwise, we
retain the null hypothesis.

The test statistic does not change from that in Example 8.1. We are testing the same population, and we measured the same value of the sample mean. We changed only the location of the rejection region in Step 2. The z statistic is the same computation as that shown in Example 8.1:

zobt =

M − µ 585 − 558
=
= 1.94 .
σM
13.9

Step 4: Make a decision. To make a decision, we compare the obtained value to the critical value. We reject the null hypothesis if the obtained value exceeds the critical value. Figure 8.7 shows that the obtained value (Zobt = 1.94) is greater than the critical value; it falls in the rejection region. The decision is to reject the null hypothesis. The p value for this test is .0262 (p = .0262). We do not double the p value for one-tailed tests.

We found in Example 8.2 that if the null hypothesis were true, then p = .0262 that we could have selected this sample mean from this population. The criteria we set in Step 2 was that the probability must be less than 5% that we obtain a sample mean, if the null hypothesis were true. Since p is less than 5%, we decide to reject the null hypothesis. We decide that the mean score on the GRE General Test in this

The test statistic reaches
the rejection region; reject
the null hypothesis.
Rejection region
α = .05

Retain the null
hypothesis

−3

−2

−1

FIGURE 8.7
0

Null

1

2

1.94

3

Since the obtained value reaches
the rejection region, we decide to
reject the null hypothesis.

18

PART III: PROBABILITY AND THE FOUNDATIONS OF INFERENTIAL STATISTICS

population is not 558, which was the value stated in the null hypothesis. Also, notice that we made two different decisions using the same data in Examples 8.1 and 8.2. This outcome is explained further in Section 8.6.

DIRECTIONAL, LOWER-TAIL CRITICAL
HYPOTHESIS TESTS (H1: 0

m1 – m2 ≠ 0

a. Which did he identify as nondirectional?
b. Which did he identify as directional?
30.The one-tailed tests. In their book, Common
Errors in Statistics (and How to Avoid Them), Good
and Hardin (2003) wrote, “No one will know
whether your [one-tailed] hypothesis was con­
ceived before you started or only after you’d
examined the data” (p. 347). Why do the
authors state this as a concern for one-tailed
tests?
31. The hopes of a researcher. Hayne Reese
(1999) wrote, “The standard method of statistical
inference involves testing a null hypothesis that
the researcher usually hopes to reject” (p. 39).
Why does the researcher usually hope to reject
the null hypothesis?
32. Describing the z test. In an article describing
hypothesis testing with small sample sizes,
Collins and Morris (2008) provided the following
description for a z test: “Z is considered signifi­
cant if the difference is more than roughly two
standard deviations above or below zero (or more

36

PART III: PROBABILITY AND THE FOUNDATIONS OF INFERENTIAL STATISTICS

precisely, |Z| > 1.96)” (p. 464). Based on this
description:
a. Are the authors referring to critical values for a
one- or two-tailed z test?
b. What alpha level are the authors referring to?
33. Sample size and power.

Collins and Morris
(2008) simulated selecting thousands of samples
and analyzed the results using many different
test statistics. With regard to the power for these
samples, they reported that “generally speaking,

all tests became more powerful as sample size
increased” (p. 468). How did increasing the sam­
ple size in this study increase power?
34. Describing hypothesis testing.

Blouin and
Riopelle (2004) made the following statement
concerning how scientists select test statistics:
“[This] test is the norm for conducting a test of H0,
when . . . the population(s) are normal with
known variance(s)” (p. 78).

Based on this descrip­
tion, what test statistic are they describing as the
norm? How do you know this?

APPENDIX C
Chapter Solutions
for Even-Numbered
End-of-Chapter Problems
C H A P T E R 8
2. Reject the null hypothesis and retain the null
hypothesis.
4.A Type II error is the probability of retaining a
null hypothesis that is actually false.
6. Critical values = ±1.96.

18.
a. a = .05.
b. a = .01.
c. a = .001.
20.
1a. Reject the null hypothesis.
1b. Reject the null hypothesis.
1c. Reject the null hypothesis.
1d. Retain the null hypothesis.
2a. Retain the null hypothesis.
2b. Retain the null hypothesis.
2c. Reject the null hypothesis.
2d. Reject the null hypothesis.

 8. All four terms describe the same thing. The
level of significance is represented by alpha,
which defines the rejection region or the region
associated with the probability of committing a
Type I error.
10. Alpha level, sample size, and effect size.
12. In hypothesis testing, the significance of an effect
determines whether an effect exists in some pop­
ulation.

Effect size is used as a measure for how
big the effect is in the population.
14. All decisions are made about the null hypothesis
and not the alternative hypothesis. The only
appropriate decisions are to retain or reject the
null hypothesis.

16. The sample size in the second sample was larger.
Therefore, the second sample had more power to
detect the effect, which is likely why the deci­
sions were different.

22.
a. σ M =

7
74 − 72
= 1.0; hence, zobt =
= 2.00.
1
49

24.

The decision is to reject the null hypothesis.
74 − 72
b. d =
= .29. A medium effect size.
7
0.05
= 0.125. A small effect size.
0.4
b. d = 0.1 = 0.25. A medium effect size.
0.4
0.4
c. d =
= 1.00. A large effect size.
0.4
a. d =

37

38

26.

PART III: PROBABILITY AND THE FOUNDATIONS OF INFERENTIAL STATISTICS

1
1
1
b. d =
2
1
c. d =
4
1
d. d =
6
a. d =

= 1.00. Large effect size.
= 0.50. Medium effect size.
= 0.25. Medium effect size.
= .17. Small effect size.

28. This will decrease standard error, thereby increas­
ing power.

30. The point Good and Hardin (2003) are making is
that it is possible with the same data to retain the
null for a two-tailed test and reject the null for a
one-tailed test where the entire rejection region is
placed in a single tail.
32.
a.Two-tailed z test.
b. a = .05.
34. We would use the z test because the population
variance is known.

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