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STUDY GUIDE
Psychology 230: STATISTICS
Unit I: Descriptive Statistics
Lesson I-1:
Statistics, Science, and Observations (CH.1, p.4)
Populations and Samples (CH.1, pp.4-10)
Objectives:
a) Distinguish between a population and a sample.
b) Distinguish between descriptive statistics and inferential statistic
Key terms:
statistics
population
sample
parameter
statistic
data
descriptive statistics
inferential statistics
sampling error
Lesson I-2:
Data Structures, Research Methods, and Statistics (CH.1, pp.10-18)
Objectives:
a) Identify two methods for obtaining observations and investigating relationships
among variables.
b) Discuss the relationship of the independent and dependent variables.
c) Distinguish between the experimental and control conditions.
Key terms:
variable
constant
correlational method
experimental method
independent variable
dependent variable
control condition
experimental condition
quasi-independent variable
Lesson I-3:
Variables and Measurement (CH.1, pp.18-24)
Objectives:
a) Give examples of discrete and continuous variables.
b) Give examples of each of the four scales of measurement.
Key terms:
construct
operational definition
discrete variable
continuous variable
real limits
nominal scale
ordinal scale
interval scale
ratio scale
Lesson I-4:
Statistical Notation (CH.1, pp.24-27)
Objectives:
a) Work through Problems 19 and 21 (p.34).
[Please Note: All answers to problems listed in these objectives can be found in
your book. These are not “assigned” problems and should not be turned in.
There are no assignments in this class. Your grade is based only on the exams.]
Key terms:
 (the Greek letter "sigma")
N
Lesson I-5:
Overview (CH.2, p.37)
Frequency Distribution Tables (CH.2, pp.36-43)
Objectives:
a) Explain the meaning of the following equation: f = N.
b) Work through Problem 1 on p.66.
c) Identify four basic rules for constructing a frequency distribution table.
Key terms:
frequency distribution
proportion
percentage
Lesson I-6:
Frequency Distribution Graphs (CH.2, pp.44-50)
The Shape of a Frequency Distribution (CH.2,pp.51-52)
Objectives:
a) Indicate when it is best to use a histogram, polygon or bar graph.
b) Do Learning Check 1 on p.51.
Key terms:
axes
normal
symmetrical distribution
skewed distribution
tail
positive skew
negative skew
PRACTICE TEST I-A
Lesson I-7:
Percentiles, Percentile Ranks, and Interpolation (CH.2, pp.52-58)
Objectives:
a) List the four steps used in the process of interpolation.
b) Do Problem 23 on p.69.
Key terms:
percentile rank
percentile
cumulative frequency (cf)
cumulative percentage (c%)
Lesson I-8:
Overview (CH.3, pp.71-73)
The Mean, part 1 (CH.3, pp.73-78)
Objectives:
a) Write out the formulas for computing a population mean and a sample mean.
b) Explain the purpose of a weighted mean.
c) Write out the formula for computing a mean from a frequency distribution
table.
d) Do Learning Check 1-5 on p.78.
Key terms:
central tendency
mean
Lesson I-9:
The Mean, part 2 (CH.3, pp.79-82)
Objectives:
a) Identify four characteristics of a mean.
b) Do Learning Check 1-3 on p.81.
Lesson I-10: The Median (CH.3, pp.82-87)
The Mode (CH.3, pp.87-89)
Objectives:
a) Identify three methods for computing a median.
b) Do Learning Check 1-5 on p.87.
c) Do Learning Check 1-3 on p.89.
Key terms:
median
mode
Lesson I-11: Selecting a Measure of Central Tendency (CH.3, pp.89-95)
Central Tendency and the Shape of the Distribution (CH.3, pp.95-97)
Objectives:
a) Indicate when to use the mode and median as measures of central tendency.
b) Identify the notation recommended in the APA manual for reporting measures
of central tendency.
c) Distinguish between positively and negatively skewed distributions.
d) Describe the relative positions of the mean, median, and mode in a skewed
distribution.
Lesson I-12: Overview (CH.4, pp.105-107)
The Range and Interquartile Range (CH.4, pp.107-109)
Objectives:
a) Identify the two purposes served by a measure of variability.
b) Do Learning Check 1 on p.109.
Key terms:
variability
range
interquartile range
semi-interquartile range
PRACTICE TEST I-B
Lesson I-13: Standard Deviation and Variance for a Population (CH.4, pp.109-116)
Objectives:
a) List the four steps used in computing a standard deviation.
b) Write out the definitional and computational formulas for SS.
c) Explain the purpose of the computational formula.
d) Write out the formulas for population variance and standard deviation.
e) Do Learning Check 3b on p.116.
Key terms:
deviation
population variance
standard deviation
sum of squares (SS)
Lesson I-14: Standard Deviation and Variance for Samples (CH.4, pp.116-121)
Objectives:
a) Write out the definitional and computational formulas for sample variance and
standard deviation.
b) Name two differences between the formulas used for a population vs. a sample.
c) Do Learning Check 1b, 2, and 3 on p.121.
Key terms:
degrees of freedom (df)
Lesson I-15: More about Variance and Standard Deviation (CH.4, pp.121-128)
Comparing Measures of Variability (CH.4, pp.128-129)
Objectives:
a) Explain what is meant by the term “unbiased” statistic.
b) Describe the role of standard deviation in descriptive statistics.
c) Indicate how adding or multiplying a constant affects standard deviation.
d) Identify the notation recommended in the APA manual for standard deviation.
e) Describe the role of variance in inferential statistics.
f) Compare the two data sets in Figure 4.10.
g) Identify the four factors that affect variability.
Lesson I-16: Introduction to z-scores (CH.5, pp.138-140)
z-Scores and Location in a Distribution (CH.5, pp.140-144)
Objectives:
a) Describe the purpose of a z-score.
b) Identify the two pieces of information contained in a z-score.
c) Write out the formula for a z-score.
d) Do Exercise 5 on p.158.
Key terms:
z-score
Lesson I-17: Using z-Scores to Standardize a Distribution (CH.5, pp.144-149)
Objectives:
a) Explain how a z-score transformation changes the shape, the mean, and the
standard deviation of a distribution.
b) Do Learning Check 1-4 on p.148.
Key terms:
standardized distribution
Lesson I-18: Other Standardized Distributions Based on z-Scores (CH.5, pp.149-151)
Computing z-Scores from Samples (CH.5, pp.151-152)
Looking Ahead to Inferential Statistics (CH.5, pp.152-154)
Objectives:
a) Describe the standardized distribution used for SAT scores.
b) Do Learning Check 1 on p.150.
PRACTICE TEST I-C
Review I-A:
Review I-B:
Objectives
Key terms
TAKE FIRST MIDTERM (25 questions, 1hour)
Unit II: Probability and Hypothesis Testing
Lesson II-1: Introduction to Probability (CH.6, pp.162-168)
Objectives:
a) State the goal of inferential statistics.
b) Explain how probability and proportion are related.
c) Describe the process of sampling with replacement.
d) Do Learning Check 1-3 on p.167.
Key terms:
probability
random sample
Lesson II-2: Probability and the Normal Distribution (CH.6, pp.168-174)
Objectives:
a) Explain how to use the unit normal table in Appendix B (p.687).
b) Distinguish between columns B and C of the unit normal table.
c) Do Learning Check 1 and 2 on p.174.
Lesson II-3: Probabilities and Proportions for Scores from a Normal Distribution
(CH.6, pp.175-181)
Objectives:
a) Identify the two steps for finding probabilities for specific X values.
b) Make sure to read Box 6.2.
c) Do Learning Check 1c, 1d, and 3 on pp.180-181.
Lesson II-4: Looking Ahead to Inferential Statistics (CH.6, pp.186-188)
Objectives:
a) Explain how probabilities can be used in inferential statistics.
Lesson II-5: Overview (CH.7, pp.196-197)
The Distribution of the Sample Means (CH.7, pp.197-204)
Objectives:
a) State the Central Limit Theorem.
b) List two ways that this theorem is of value.
c) Describe the shape, mean and variability of a sampling distribution.
d) Identify two factors that affect the numerical value of the standard error.
e) Write the formula for the standard error.
Key terms:
sampling error
distribution of sample means
sampling distribution
expected value of M
standard error of M
law of large numbers
Lesson II-6: Probability and the Distribution of Sample Means (CH.7, pp.205-208)
Objectives:
a) Identify the primary use of the distribution of sample means.
b) Do Learning Check 1a, 1c and 2a on p.208.
PRACTICE TEST II-A
Lesson II-7: More about Standard Error (CH.7, pp.208-213)
Objectives:
a) Distinguish between sampling error and standard error.
b) Identify the main difference between samples of n=1, n=4, and n=100 (Fig.7.7).
c) Identify the notation used in journals for standard error.
d) Do Learning Check 1-3 on p.213.
Lesson II-8: Looking Ahead to Inferential Statistics (CH.7, pp.213-217)
Objectives:
a) Give an example of the use of standard error in inferential statistics.
b) Explain how standard error can be used as a measure of reliability.
c) Do Learning Check 1 on p.217.
Lesson II-9: The Logic of Hypothesis Testing (CH.8, pp.226-237)
Objectives:
a) Describe the four steps of hypothesis testing.
b) Explain the logic of hypothesis testing presented in Box 8.1.
c) Describe the role of z-scores in hypothesis testing.
d) Do Learning Check 1, 3, 4, and 5 on p.237.
Key terms:
hypothesis test
null hypothesis
alternative hypothesis
alpha level
critical region
Lesson II-10: Uncertainty and Errors in Hypothesis Testing (CH.8, pp.237-241)
Objectives:
a) Distinguish between Type I and Type II error.
b) Do Learning Check 2, 3, and 5 on p.246.
Key terms:
Type I error
Type II error
Lesson II-11: An Example of a Hypothesis Test (CH.8, pp.241-249)
Objectives:
a) Explain the meaning of the statement "The treatment with medication had a
significant effect on people's depression scores, z = 2.45, p < .05" (p.249).
b) Distinguish p < .05 and p > .05.
c) Identify four assumptions that must hold true for hypothesis tests with z-scores.
d) Do Learning Check 1-3 on p.252.
Key terms:
statistically significant
Lesson II-12: Directional (One-Tailed) Hypothesis Tests (CH.8, pp.250-254)
The General Elements of Hypothesis Testing (CH.8, pp.254-256)
Objectives:
a) Indicate the conditions under which a one-tailed test would be used.
b) Compare one-tailed and two-tailed tests.
c) Do Learning Check 1 and 2 on p.254.
d) Describe each of the five elements of a hypothesis test.
Key terms:
one-tailed test
PRACTICE TEST II-B
Lesson II-13: Concerns about Hypothesis Testing (CH.8, pp.256-260)
Statistical Power (CH.8, pp.260-264)
Objectives:
a) Discuss the common criticisms of hypothesis testing.
b) Explain the function of Cohen’s d.
c) Identify the relationship between power and Type II error.
d) Describe three ways that the experimenter can affect the power of a test.
e) Do Learning Check 1-4 on p.264.
Key terms:
power
Lesson II-14: The t Statistic--An Alternative to z (CH.9, pp.275-281)
Objectives:
a) Identify a shortcoming of using the z-score as an inferential statistic.
b) Write the formula for the t statistic.
c) Give two reasons why standard error is written as a function of variance rather
than of standard deviation.
d) Indicate how the t distribution changes as a function of df.
e) Learn to use the t distribution table in Appendix B (p.691).
f) Do Learning Check 1-5 on pp.280-281.
Key terms:
estimated standard error
t statistic
degrees of freedom
Lesson II-15: Hypothesis Tests with the t Statistic (CH.9, pp.281-285)
Measuring Effect Size for the t Statistic (CH.9, pp.285-291)
Directional Hypotheses and One-Tailed Tests (CH.9, pp.291-293)
Objectives:
a) Name the four steps involved in hypothesis testing with the t statistic.
b) Describe the APA format for reporting the results of a t test.
c) Identify the two assumptions needed for hypothesis tests with the t statistic.
d) Do Learning Check 1 on p.292.
Lesson II-16: Overview (CH.10, pp.302-303)
The t Statistic for an Independent-Measure Research Design
(CH.10, pp.304-311)
Objectives:
a) Give an example of an experiment comparing two independent samples.
b) Distinguish between within-subjects and between-subjects designs.
c) Compare the formulas for the single-sample and independent-measures t
statistics.
d) Explain the purpose of pooled variance.
e) Write out the complete formulas for pooled variance and for the independentmeasures t statistic.
f) Do Learning Check 1-4 on p.310.
Key terms:
independent-measures (between-subjects) design
Lesson II-17: Hypothesis Tests and Effect Size with the Independent-Measures t Statistic
(CH.10, pp.311-320)
Objectives:
a) Name the four steps in hypothesis-testing with an independent-measures t.
b) Describe the APA format for reporting an independent-measures t test.
c) Do Learning Check 1-3 on pp.318.
d) Compare the data in Figure 10.6 and Figure 10.7.
Lesson II-18: Factors Influencing the Independent-Measures t (CH.10, pp.326-330)
Assumptions Underlying the Independent-Measures t (CH.10, pp.330-332)
Objectives:
a) Name three assumptions of the independent-measures t test.
b) Do Problem 25a (p.332).
PRACTICE TEST II-C
Review II-A: Objectives
Review II-B: Key terms
TAKE SECOND MIDTERM (25 questions, 1 hour)
Unit III: Repeated Measures, ANOVA and Regression
Lesson III-1: Overview (CH.11, pp.334-336)
The t Statistic for Related Samples (CH.11, pp.336-339)
Objectives:
a) Give an example of a repeated-measures study.
b) Name an advantage of the repeated-measures design.
c) Write out the formula for the repeated-measures t statistic.
d) Do Learning Check 1-3 on p.339.
Key terms:
repeated-measures design
matched-subjects design
Lesson III-2: Hypothesis Tests for the Repeated-Measures Design (CH.11, pp.340-346)
Objectives:
a) Name the four steps of hypothesis-testing using the repeated-measures t test.
b) Describe the APA format for reporting a repeated-measures t test.
c) Do Learning Check 1 and 3 on pp.345-346.
Lesson III-3: Introduction (CH.13, pp.389-393)
The Logic of Analysis of Variance (CH.13, pp.393-397)
Objectives:
a) Identify the main advantage that ANOVA has over t tests.
b) Explain why ANOVA uses variance instead of mean differences.
c) Distinguish between-treatments and within-treatments variance.
d) Identify two possible explanations for between-groups variance.
e) Identify two primary sources of chance differences.
f) Do Learning Check 1-4 on p.397.
Key terms:
factor
level
F-ratio
between-treatments variability
within-treatments variability
Lesson III-4: ANOVA Notation and Formulas (CH.13, pp.397-405)
Objectives:
a) Define k, n, N, T, and G.
b) Compare SStotal, SSwithin, and SSbetween.
c) Compare dftotal, dfwithin, and dfbetween.
d) Write out the formulas for MSbetween, MSwithin, and F.
e) Do Learning Check 1-3 on p.405.
Lesson III-5: The Distribution of F-Ratios (CH.13, pp.406-407)
Objectives:
a) Name two characteristics of the distribution of F-ratios.
Lesson III-6: Examples of Hypothesis Testing and Effect Size with ANOVA
(CH.13, pp.408-418)
Objectives:
a) Describe the APA format for reporting an ANOVA.
b) Explain what the numerator and the denominator of the F-ratio represent.
c) Compare the two sets of data in Figure 13.10.
d) Do Learning Check 1 and 2 on pp.417-418.
Key terms:
error term
PRACTICE TEST III-A
Lesson III-7: Post Hoc Tests (CH.13, pp.418-423)
The Relationship between ANOVA and t Tests (CH.13, pp.423-425)
Objectives:
a) Describe the function of post hoc tests.
b) Write out the formula for Tukey's HSD.
c) Explain how to use Tukey's HSD and the Scheffe test.
d) Write out the mathematical relationship between F and t.
e) Do Learning Check 1-3 on pp.422-423 and Learning Check 1-2 on p.425.
Key terms:
post hoc test
experimentwise alpha level
Lesson III-8: Overview (CH.15, pp.466-468)
Main Effects and Interactions, part 1 (CH.15, pp.468-471)
Objectives:
a) Identify the three types of mean differences that can occur in Example 15.1.
b) Identify the three null hypotheses for Example 15.1.
Key terms:
main effect
interaction
Lesson III-9: Main Effects and Interactions, part 1 (CH.15, pp.471-476)
Objectives:
a) Explain how two factors are related when there is an interaction between them.
b) Indicate how you can tell from a graph of a two-factor ANOVA whether or not
there is a significant interaction between the two factors.
c) Compare the three sets of data in Table 15.4.
d) Do Learning Check 1-4 on p.476.
Lesson III-10: Notation and Formulas (CH.15, pp.476-485)
Objectives:
a) Describe the two stages of a two-factor ANOVA.
b) Do Learning Check 1 on pp.483.
c) Describe the APA format for reporting a two-factor ANOVA.
Lesson III-11: Interpreting the Results from a Two-Factor ANOVA (CH.15, pp.485-489)
Objectives:
a) In Example 15.3, compare the main effect of task difficulty for each of the three
arousal conditions.
Lesson III-12: Assumptions for the Two-Factor ANOVA (CH.15, pp.489-490)
Objectives:
a) List three assumptions for a two-factor ANOVA.
PRACTICE TEST III-B
Lesson III-13: Overview (CH.16, pp.506-511)
Objectives:
a) Name three characteristics of a relationship that can be measured by a
correlation.
b) Give four reasons why a correlation might be used.
c) Do Learning Check 1-4 on pp.510-511.
Key terms:
correlation
positive correlation
negative correlation
Lesson III-14: The Pearson Correlation (CH.16, pp. 511-516)
Objectives:
a) Define r and SP.
b) Write out the formula for Pearson's r.
c) Do Learning Check 1-4 on pp.515-516.
Key terms:
Pearson correlation
Lesson III-15:Understanding and Interpreting the Pearson Correlation
(CH.16, pp.516-521)
Objectives:
a) State the relationship between correlation and causation.
b) Explain the effects of restricted range and outriders on a correlation.
c) Do Exercise 11 on p.544.
Key terms:
coefficient of determination
Lesson III-16:Hypothesis Tests with the Pearson Correlation (CH.16, pp.521-525)
Objectives:
a) Indicate when hypothesis testing is used with Pearson's r.
b) Describe the APA format for reporting correlations.
c) Do Learning Check 1-3 on p.525 and Exercise 13 on pp.544-545.
Lesson III-17:Introduction to Regression, part 1 (CH.17, pp.549-557)
Objectives:
a) Describe the goal of linear regression.
b) Do Learning Check 1-3 on p.552.
c) Write out the least-squares solutions for a (intercept) and b (slope).
d) Do Learning Check 1 on p.556.
Key terms:
regression
regression equation for Y
Lesson III-18:Introduction to Regression, part 2 (CH.16, pp.557-561)
Objectives:
a) Explain the function of the standard error of estimate.
b) Write out the formula for the standard error of estimate as a function of r.
c) Do Learning Check 1 and 2 on p. 561.
Key terms:
standard error of estimate
PRACTICE TEST III-C
Review III-A: Objectives
Review III-B: Key terms
TAKE FINAL EXAM (50 questions, 2 hours)
Practice Test I-A
1.
Using letter grades (A, B, C, D, and F) to classify student performance on an exam is an
example of measurement on a(n) _____________ scale of measurement.
A.
nominal
B.
ordinal
C.
interval
D.
ratio
2.
Although research questions typically concern a _____________, a research study
typically examines a _____________.
A.
sample, population
B.
statistic, sample
C.
population, sample
D.
statistic, population
3.
In an experiment, the researcher manipulates the _____________ variable and measures
changes in the _____________ variable.
A.
population, sample
B.
sample, population
C.
dependent, independent
D.
independent, dependent
4.
A.
B.
C.
D.
What is X2 for the following set of scores: 3, 0, 5, 2.
20
38
100
400
5.
In the table of data shown below, the percentage of scores with X = 8 is
X
10
9
8
7
6
5
4
A.
B.
C.
D.
14%
24%
32%
60%
f_
5
7
6
3
2
0
2
6.
A.
B.
C.
D.
Frequency distribution polygons are intended for use with
only ratio scales.
either nominal or ordinal scales.
only nominal scales.
either interval or ratio scales.
Practice Test I-B
1.
In the table below, what is the percentile rank corresponding to a score of X = 19.5?
X
25-29
20-24
15-19
10-14
5- 9
A.
B.
C.
D.
f
1
6
8
7
3
cf
25
24
18
10
3
c%__
100%
96%
72%
40%
12%
18%
40%
72%
96%
2.
A.
B.
C.
D.
Which of the following is NOT an advantage of a stem and leaf display?
provides both a listing of the scores and a picture of the distribution
easy to construct
can be used to display nominal and ordinal data
allows you to identify every individual score in the data set
3.
In a sample of n = 6, five individuals all have scores of X = 10 and the sixth person has a
score of X = 16. What is the mean for this sample?
A.
11
B.
13
C.
13.2
D.
not enough information to find the mean
4.
What is the median for the following set of scores?
Scores:
1, 3, 9, 10, 22
A.
6
B.
9
C.
9.5
D.
11
5.
A teacher gave a reading test to a class of 5th-grade students and computed the mean,
median, and mode for the test scores. Which of the following CANNOT be an accurate
description of the scores?
A.
The majority of the students had scores above the median.
B.
The majority of the students had scores above the mean.
C.
The majority of the students had scores above the mode.
D.
All of the above must be false statements.
6.
Which of the numbers below represents the range for the following data set?
Scores:
1, 3, 9, 10, 22
A.
20
B.
21
C.
22
D.
23
Practice Test I-C
1.
The computational formula for sum of squares is
A.
 (X – M)2 , where M is the mean of X
B.
 (X – M)2 , where M is the mean of X
n
X2 - (X)2
C.
n
D.
X - (X) 2
n
2.
A.
B.
C.
D.
For the population of scores, 5 2 5 4, the variance equals
6
2
1.22
1.5
3.
A.
The correct formula for the sample standard deviation is
SS
n-1
B.
SS
N
___
SS
df
___
SS
N
C.
D.
2
4.
A.
B.
C.
D.
For a population with  = 80 and  = 10, the z-score corresponding to X = 85 would be
+0.50
+1.00
+5.00
80/10
5.
A.
B.
C.
D.
A z-score of z = +2.00 indicates a position in a distribution that is located
above the mean by 2 points.
at a value that is exactly 2 times the population standard deviation.
at a value that is exactly 2 times the population mean.
above the mean by a distance that is exactly 2 times the population standard deviation.
6.
Suppose you earned a score of X = 53 on an exam. Which set of parameters would give
you the highest grade?
A.
 = 50 and  = 3
B.
 = 50 and  = 1
C.
 = 56 and  = 3
D.
 = 56 and  = 1
Practice Test II-A
1.
A.
B.
C.
D.
The goal of inferential statistics is to
begin with a population and then answer specific questions about a sample.
begin with a sample and then answer general questions about a population.
begin with central tendency and then answer questions about variability.
begin with variability and then answer questions about central tendency.
2.
A.
B.
C.
D.
What proportion of the scores in a normal distribution is below z = 0.86
0.1949
0.3051
0.6949
0.8051
3.
A.
B.
C.
D.
For a normal distribution with  = 90 and  = 5, what is the percentile rank for X = 84?
11.51%
38.49%
54.00%
88.49%
4.
A normal distribution has a mean of  = 36 with  = 4. What proportion of the
distribution falls between scores of X = 30 and X = 38?
A.
0.2583
B.
0.3753
C.
0.6247
D.
0.7417
5.
A.
B.
C.
D.
The distribution of sample means (for a specific sample size) consists of
all the scores contained in the sample.
all the scores contained in the population.
the specific sample mean computed for one sample of scores.
all the sample means that could be obtained (for the specific sample size).
6.
For a population with  = 80 with  = 20, the distribution of sample means for a sample
size of n = 4 will have a standard error of
A.
5
B.
10
C.
20
D.
80
Practice Test II-B
1.
is that
A.
B.
C.
D.
The difference between the distribution of sample means for samples of n = 2 and n =20
the variability is greater for samples of n = 2.
the variability is greater for samples of n = 20.
the mean is greater for samples of n = 2.
the mean is greater for samples of n = 20.
2.
A random sample of n = 4 scores is obtained from a normal population with  = 20 and
with  = 4. What is the probability of obtaining a mean greater than 22 for this sample?
A.
0.50
B.
1.00
C.
0.1587
D.
0.3085
3.
A.
B.
C.
D.
A researcher risks a Type I error
anytime H0 is rejected.
anytime H1 is rejected.
anytime the decision is “fail to reject H0.”
all of the above.
4.
A.
B.
C.
D.
The final step of hypothesis testing is to
locate the values associated with the critical region.
make a statistical decision about H0.
collect the sample data and compute the test statistic.
state the hypotheses and select alpha.
5.
A critical region begins at z = +1.96 and z = -1.96. The obtained z-score for the sample
data is z = -1.90. The correct statistical decision is
A.
fail to reject H1.
B.
fail to reject H0.
C.
reject H1.
D.
reject H0.
6.
A researcher expects a treatment to produce an increase in the population means.
Assuming a normal distribution, what is the critical z-score for a one-tailed test with  = .01?
A.
2.33
B.
2.58
C.
+1.65
D.
+2.33
Practice Test II-C
1.
A.
B.
C.
D.
The power of a statistical test is expressed as


1-
1-
2.
A.
B.
C.
D.
In what circumstances is the t statistic used instead of a z-score for hypothesis testing?
when the sample size is n = 30 or greater
when the population mean is unknown
when the population variance or standard deviation is unknown
if you are not sure whether or not the population distribution is normal
3.
With  = .05, the two-tailed critical region for a sample of n = 10 subjects would have
boundaries of
A.
t = 2.262
B.
t = 2.228
C.
t = 1.96
D.
t = 1.833
4.
A.
B.
C.
D.
One clue to identifying a situation calling for an independent-measures t is
the value for  should be known.
the mean for a treated group of subjects is compared to a known population mean.
one sample is used to test a hypothesis about one population.
there are two samples containing different subjects.
5.
A researcher reports t (24) = 5 for an independent-measures experiment. How many
subjects participated in this experiment?
A.
26
B.
24
C.
12
D.
4
6.
For the data shown below, the independent-measures t statistic is equal to
Treatment 1
n=6
mean = 10
SS = 70
A.
B.
C.
D.
-1.00
-1.41
-2.00
+2.83
Treatment 2
n=6
mean = 14
SS = 50
Practice Test III-A
1.
A repeated-measures study would not be appropriate for which of the following
situations?
A.
A researcher would like to study the effect of practice on performance.
B.
A researcher would like to compare individuals from two different populations.
C.
The effect of a treatment is studied in a small group of individuals with a rare disease.
C.
A developmental psychologist examines how behavior unfolds by observing the same
group of children at different ages.
2.
A.
B.
C.
D.
What is the value of SD for the following set of n = 9 D-scores with SS = 72?
72
9
3
1
3.
For an experiment comparing more than two treatment conditions, you should use
analysis of variance rather than separate t tests because
A.
you are less likely to make a mistake in the computations of ANOVA.
B.
a test based on variance is more sensitive than a test based on means.
C.
ANOVA has less risk of a Type I error.
D.
ANOVA has less risk of a Type II error.
4.
For an independent-measures experiment comparing two treatment conditions with a
sample of n = 10 in each treatment, the F-ratio would have df equal to
A.
18
B.
19
C.
1, 18
D.
1, 19
5.
A.
B.
C.
D.
For an F-ratio with df = 2, 10, the critical value for a hypothesis test using  = .05 is
4.10
7.56
19.39
99.40
6.
In the analysis of the data shown below, SSwithin is equal to:
Treatment 1
Treatment 2
Treatment 3
n=5
n=5
n=5
T=5
T=10
T=15
SS=25
SS=20
SS=15
60
15
10
cannot be determined from the information given
A.
B.
C.
D.
Practice Test III-B
1.
A.
B.
C.
D.
Post hoc tests are necessary after an ANOVA whenever
H0 is rejected.
there are more than two treatments.
a and b
You should always do post hoc tests after an ANOVA.
2.
A.
B.
C.
D.
Two variables are said to interact when
the effect of one independent variable depends on the level of the second variable.
both variables are equally influenced by a third factor.
the two variables are differentially affected by a third variable.
both variables produce a change in the subjects’ scores.
Questions 3 and 4 refer to the following set of data. Note that the values shown in the matrix are
treatment means. Also note that one of the cell means is missing.
Factor B
B1
B2
A1
M=6
M=8
A2
M=12
M=??
Factor A
3.
A.
B.
C.
D.
What value for the missing mean would result in no main effect for Factor A (SSA=0)?
2
10
12
14
4.
A.
B.
C.
D.
What value for the missing mean would result in no interaction (SSAXB=0)?
2
10
12
14
5.
For a two-factor experiment with 2 levels of factor A and 3 levels of factor B and n=10
subjects in each treatment condition, there is a total of ____ subjects in each level of factor A and
a total of ____ subjects in each level of factor B.
A.
10, 10
B.
20, 30
C.
30, 20
D.
60, 60
6.
In a two-factor experiment, the F-ratio for factor A has df = 1,32 and the F-ratio for factor
B has df = 2, 32. For this experiment, the F-ratio for the AxB interaction would have
A.
df = 1,32
B.
df = 2,32
C.
df = 3, 32
D.
cannot be determined from the information given
Practice Test III-C
1.
A.
B.
C.
D.
A Pearson correlation of r = -0.85 indicates that a graph of the data would show
points clustered close to a line that slopes up to the right.
points clustered close to a line that slopes down to the right.
points widely scattered around a line that slopes up to the right.
points widely scattered around a line that slopes down to the right.
2.
A set of n = 5 pairs of X and Y values has X = 10, Y = 20, and XY = 60. For this
set of scores, the value of SP is
A.
-20
B.
-28
C.
20
D.
60
3.
Suppose the correlation between height and weight for adults is +0.80. What percent of
the variability in weights is due to the relationship with height?
A.
80%
B.
64%
C.
100 – 80 = 20%
D.
40%
Questions 4 and 5 concern the following data:
X
Y
2
4
5
2
3
5
2
5
4.
For these data, SP equals
A.
6
B.
-5
C.
36
D.
none of the above
5.
A.
B.
C.
D.
For these data, the Pearson correlation
is negative.
is positive.
is zero.
cannot be determined with the information provided.
6.
A.
B.
C.
D.
In the linear equation Y = 3X + 1, when X increases by 4 points, Y will increase by
4 points.
7 points.
12 points.
13 points.
Answer Key
Practice Test I-A
1.
b
2.
c
3.
d
4.
b
5.
b
6.
d
Practice Test I-B
1.
c
2.
c
3.
a
4.
b
5.
a
6.
c
Practice Test I-C
1.
c
2.
d
3.
c
4.
a
5.
d
6.
b
Practice Test II-A
1.
b
2.
d
3.
a
4.
c
5.
d
6.
b
Practice Test II-B
1.
a
2.
c
3.
a
4.
b
5.
b
6.
d
Practice Test II-C
1.
d
2.
c
3.
a
4.
d
5.
a
6.
c
Practice Test III-A
1.
b
2.
d
3.
c
4.
c
5.
a
6.
a
Practice Test III-B
1.
c
2.
a
3.
a
4.
d
5.
c
6.
b
Practice Test III-C
1.
b
2.
c
3.
b
4.
b
5.
a
6.
c
28