Question 1

If the group variances are unequal, you should use the unequal variance ANOVA test.

Accepted Answer

A) True 
 B)False  False

Question 2

[Note to Instructor: Use this question only if you are combining questions from all inferential statistics chapters.] If you have four groups with equal sample sizes and an interval-level dependent variable, what statistic would you use to determine if a relationship exists?

Accepted Answer

A)  the maximum-likelihood chi-square 
B)  analysis of variance 
C)  equal variance t-test 
D)  Fisher's exact test 
A)  the maximum-likelihood chi-square 
B)  analysis of variance 
C)  equal variance t-test 
D)  Fisher's exact test B

Question 3

Only a one-tailed hypothesis should be used with the analysis of variance statistic.

Accepted Answer

A) True 
 B)False  False

Question 4

The ANOVA cannot be used for a 2-group comparison.

Accepted Answer

A) True 
 B)False

Question 5

How many groups can be used in the analysis of variance?

Accepted Answer

A)  4 
B)  as many as you want 
C)  only 2 
D)  between 3 and 15 
A)  4 
B)  as many as you want 
C)  only 2 
D)  between 3 and 15

Question 6

A determination of which groups, if any, are significantly different from each other cannot be done with the analysis of variance test.

Accepted Answer

A) True 
 B)False

Question 7

Multiple comparison tests compare only the highest and lowest group values. All others have to be estimated using.

Accepted Answer

A) True 
 B)False

Question 8

A reasonable alternative to the ANOVA statistic is the

Accepted Answer

A)  Chi-square test 
B)  Kruskal-Wallis H test 
C)  Mann-Whitney U test 
D)  Fisher's Exact test 
A)  Chi-square test 
B)  Kruskal-Wallis H test 
C)  Mann-Whitney U test 
D)  Fisher's Exact test

Question 9

If an analysis of variance is significant, you should use a multiple comparison test to locate groups with significantly different comparisons.

Accepted Answer

A) True 
 B)False

Question 10

If data are not normally distributed, which of the two inferential tests below should not be used?

Accepted Answer

A)  Chi-square and t-test 
B)  Chi-square and analysis of variance 
C)  T-test and analysis of variance 
D)  All of the tests can be used, a normal distribution is not required for any of them. 
A)  Chi-square and t-test 
B)  Chi-square and analysis of variance 
C)  T-test and analysis of variance 
D)  All of the tests can be used, a normal distribution is not required for any of them.

Question 11

The analysis of variance is designed for which level of measurement?

Accepted Answer

A)  nominal 
B)  ordinal 
C)  interval 
D)  ratio 
A)  nominal 
B)  ordinal 
C)  interval 
D)  ratio

Question 12

An independent variable with 6 values (groups) can be used with a the t-test, but you would have to make several ?t-tests. Of the various problems below, which is the one reflected by making multiple t-tests?

Accepted Answer

A)  you would have an experiment-wise error rate problem 
B)  the groups would present a normal-distribution problem 
C)  there is no way to choose correctly which t-test you should use 
D)  there is no problem with making multiple t-tests as long as you meet other assumptions 
A)  you would have an experiment-wise error rate problem 
B)  the groups would present a normal-distribution problem 
C)  there is no way to choose correctly which t-test you should use 
D)  there is no problem with making multiple t-tests as long as you meet other assumptions

Question 13

The concept of "error" is expressed in the ANOVA by

Accepted Answer

A)  the total mean sums 
B)  the between sums of squares 
C)  the within sums of squares 
D)  the between mean squares 
A)  the total mean sums 
B)  the between sums of squares 
C)  the within sums of squares 
D)  the between mean squares

Question 14

One of the assumptions of the Analysis of Variance test is homogeneity of variances. If the data violate this assumption they are referred to as:

Accepted Answer

A)  nonhomogenic 
B)  heteroscedastic 
C)  homoscedastic 
D)  lacking variation 
A)  nonhomogenic 
B)  heteroscedastic 
C)  homoscedastic 
D)  lacking variation

Question 15

Which reason below best answers why a two-tailed hypothesis should be used with an analysis of variance.

Accepted Answer

A)  the probability levels for ANOVA do not easily divide by 2 as in other tests 
B)  the dependent variable in an ANOVA is nominal level 
C)  the normal distribution assumption is not used, so a one-tailed curve does not exist 
D)  it is too difficult to make predictions for multiple group comparisons in a single statement 
A)  the probability levels for ANOVA do not easily divide by 2 as in other tests 
B)  the dependent variable in an ANOVA is nominal level 
C)  the normal distribution assumption is not used, so a one-tailed curve does not exist 
D)  it is too difficult to make predictions for multiple group comparisons in a single statement

Question 16

If you are doing 20 two-at-a-time t-tests, you would expect 1 test result to be the result of error.

Accepted Answer

A) True 
 B)False

Question 17

The analysis of variance is part of the nonlinear specific model, a family of statistical techniques.

Accepted Answer

A) True 
 B)False

Question 18

If there are multiple violations of the assumptions of ANOVA, it doesn't really make a difference because ANOVA is very robust to these violations.

Accepted Answer

A) True 
 B)False

Question 19

The best approach, of those listed below, to determining differences between individual groups is to use

Accepted Answer

A)  the analysis of variance test 
B)  the Bonferroni test 
C)  the t-test 
D)  the Mann-Whitney U test 
A)  the analysis of variance test 
B)  the Bonferroni test 
C)  the t-test 
D)  the Mann-Whitney U test

Question 20

The effect of an independent variable in the ANOVA is found

Accepted Answer

A)  in the degrees of freedom used to create the F-ratio value 
B)  in the product of the between mean squares divided by the within mean squares 
C)  in the product of the between sums of squares divided by the within sums of squares 
D)  you can't find this affect in ANOVA, you have to use a multiple comparison test 
A)  in the degrees of freedom used to create the F-ratio value 
B)  in the product of the between mean squares divided by the within mean squares 
C)  in the product of the between sums of squares divided by the within sums of squares 
D)  you can't find this affect in ANOVA, you have to use a multiple comparison test

If the group variances are unequal, you should use the unequal variance ANOVA test.

[Note to Instructor: Use this question only if you are combining questions from all inferential statistics chapters.] If you have four groups with equal sample sizes and an interval-level dependent variable, what statistic would you use to determine if a relationship exists?

Only a one-tailed hypothesis should be used with the analysis of variance statistic.

The ANOVA cannot be used for a 2-group comparison.

How many groups can be used in the analysis of variance?

A determination of which groups, if any, are significantly different from each other cannot be done with the analysis of variance test.

Multiple comparison tests compare only the highest and lowest group values. All others have to be estimated using.

A reasonable alternative to the ANOVA statistic is the

If an analysis of variance is significant, you should use a multiple comparison test to locate groups with significantly different comparisons.

If data are not normally distributed, which of the two inferential tests below should not be used?

The analysis of variance is designed for which level of measurement?

An independent variable with 6 values (groups) can be used with a the t-test, but you would have to make several ?t-tests. Of the various problems below, which is the one reflected by making multiple t-tests?

The concept of "error" is expressed in the ANOVA by

One of the assumptions of the Analysis of Variance test is homogeneity of variances. If the data violate this assumption they are referred to as:

Which reason below best answers why a two-tailed hypothesis should be used with an analysis of variance.

If you are doing 20 two-at-a-time t-tests, you would expect 1 test result to be the result of error.

The analysis of variance is part of the nonlinear specific model, a family of statistical techniques.

If there are multiple violations of the assumptions of ANOVA, it doesn't really make a difference because ANOVA is very robust to these violations.

The best approach, of those listed below, to determining differences between individual groups is to use

The effect of an independent variable in the ANOVA is found

Introduction to Statistics

Levels of Measurement

Graphical Statistics

Measures of Central Tendency

Measures of Dispersion

Curves and Distributions

Frequency Distributions

Elementary Relationships: Crosstabulation Tables

Hypotheses and Sampling Distributions

Statistical Significance

Testing for Significance: the Chi-Square Test

Testing for Significance in Two Groups: The T-Test

The Concept of Association

Testing for Association: Phi

Testing for Association: Pearsons R and Regression

Doing Real Research: Elementary Multivariate Relationships

Statistics and Data Analysis

Filters

Exam 13: Testing for Significance in Multiple Groups: The Analysis of Variance Statistic

If the group variances are unequal, you should use the unequal variance ANOVA test.

[Note to Instructor: Use this question only if you are combining questions from all inferential statistics chapters.] If you have four groups with equal sample sizes and an interval-level dependent variable, what statistic would you use to determine if a relationship exists?

Only a one-tailed hypothesis should be used with the analysis of variance statistic.

The ANOVA cannot be used for a 2-group comparison.

How many groups can be used in the analysis of variance?

A determination of which groups, if any, are significantly different from each other cannot be done with the analysis of variance test.

Multiple comparison tests compare only the highest and lowest group values. All others have to be estimated using.

A reasonable alternative to the ANOVA statistic is the

If an analysis of variance is significant, you should use a multiple comparison test to locate groups with significantly different comparisons.

If data are not normally distributed, which of the two inferential tests below should not be used?

The analysis of variance is designed for which level of measurement?

An independent variable with 6 values (groups) can be used with a the t-test, but you would have to make several ?t-tests. Of the various problems below, which is the one reflected by making multiple t-tests?

The concept of "error" is expressed in the ANOVA by

One of the assumptions of the Analysis of Variance test is homogeneity of variances. If the data violate this assumption they are referred to as:

Which reason below best answers why a two-tailed hypothesis should be used with an analysis of variance.

If you are doing 20 two-at-a-time t-tests, you would expect 1 test result to be the result of error.

The analysis of variance is part of the nonlinear specific model, a family of statistical techniques.

If there are multiple violations of the assumptions of ANOVA, it doesn't really make a difference because ANOVA is very robust to these violations.

The best approach, of those listed below, to determining differences between individual groups is to use

The effect of an independent variable in the ANOVA is found

Introduction to Statistics

Levels of Measurement

Graphical Statistics

Measures of Central Tendency

Measures of Dispersion

Curves and Distributions

Frequency Distributions

Elementary Relationships: Crosstabulation Tables

Hypotheses and Sampling Distributions

Statistical Significance

Testing for Significance: the Chi-Square Test

Testing for Significance in Two Groups: The T-Test

The Concept of Association

Testing for Association: Phi

Testing for Association: Pearsons R and Regression

Doing Real Research: Elementary Multivariate Relationships

Statistics and Data Analysis

Filters