Question 1

Mistakenly rejecting the null hypothesis when it is true and should not have been rejected is technically called a(n)

Accepted Answer

A)  Type I error. 
B)  Type II error. 
C)  alpha error. 
D)  beta error. 
A)  Type I error. 
B)  Type II error. 
C)  alpha error. 
D)  beta error.

Question 2

Power is equal to

Accepted Answer

A)  alpha. 
B)  beta. 
C)  1 minus alpha. 
D)  1 minus beta. 
A)  alpha. 
B)  beta. 
C)  1 minus alpha. 
D)  1 minus beta.

Question 3

When the alternative hypothesis makes a specific prediction, one should use

Accepted Answer

A)  the null p value. 
B)  a two-tailed p value. 
C)  a one-tailed p value. 
D)  an alternative p value. 
A)  the null p value. 
B)  a two-tailed p value. 
C)  a one-tailed p value. 
D)  an alternative p value.

Question 4

Why does "nonsignificance" not mean the same thing as "no effect" in null hypothesis significance testing?

Accepted Answer

"Nonsignificance" and "no effect" do not

Question 5

At the beginning of a study, the researcher hypothesizes that there is no difference between the experimental and control groups. This is an example of a(n)

Accepted Answer

A)  Type I error. 
B)  null hypothesis. 
C)  alternative hypothesis. 
D)  significant hypothesis. 
A)  Type I error. 
B)  null hypothesis. 
C)  alternative hypothesis. 
D)  significant hypothesis.

Question 6

Which of the following is NOT a name for the probability of making a Type I error?

Accepted Answer

A)  alpha 
B)  significance level 
C)  beta 
D)  p value 
A)  alpha 
B)  significance level 
C)  beta 
D)  p value

Question 7

When would one use a one-tailed p value rather than a two-tailed p value? Why would one prefer to use a one-tailed p value in these situations?

Accepted Answer

One would use a one-tailed p value rathe

Question 8

____________ tells us the nonnull magnitude of the effect size that is supported by exactly the same amount of evidence as is the null value of the effect size.

Accepted Answer

A)  The chi-square statistic 
B)  The Pearson's r 
C)  The counternull statistic 
D)  The coefficient of determination 
A)  The chi-square statistic 
B)  The Pearson's r 
C)  The counternull statistic 
D)  The coefficient of determination

Question 9

Why are Type I and Type II errors characterized respectively as being errors of gullibility and blindness to a relationship?

Accepted Answer

Type I and Type II errors are concepts f

Question 10

What are the two common errors in thinking about effect sizes that the counternull statistic helps the researcher minimize? Is this the same as Type I and Type II errors?

Accepted Answer

The counternull statistic is a concept i

Question 11

How can a statistical power analysis be useful when planning an experimental study?

Accepted Answer

A statistical power analysis is an essen

Question 12

Which of the following refers to the probability of making a Type II error?

Accepted Answer

A)  significance level 
B)  beta 
C)  alpha 
D)  p-value 
A)  significance level 
B)  beta 
C)  alpha 
D)  p-value

Question 13

The counternull statistic is useful for minimizing which of the following common errors (in thinking about effect sizes)?

Accepted Answer

A)  the ecological fallacy 
B)  inferring that a failure to reject the null hypothesis also implies an effect size of zero 
C)  error of equating the rejection of the null hypothesis with having demonstrated a scientifically important effect 
D)  b and c 
A)  the ecological fallacy 
B)  inferring that a failure to reject the null hypothesis also implies an effect size of zero 
C)  error of equating the rejection of the null hypothesis with having demonstrated a scientifically important effect 
D)  b and c

Question 14

Why would one want to calculate the confidence interval associated with an observed effect size?

Accepted Answer

Calculating the confidence interval asso

Question 15

The initials "NHST" stand for

Accepted Answer

A)  Normal HypothesiS Testing. 
B)  Null HypothesiS Testing. 
C)  Normal Hypothesis Significance Testing. 
D)  Null Hypothesis Significance Testing. 
A)  Normal HypothesiS Testing. 
B)  Null HypothesiS Testing. 
C)  Normal Hypothesis Significance Testing. 
D)  Null Hypothesis Significance Testing.

Mistakenly rejecting the null hypothesis when it is true and should not have been rejected is technically called a(n)

Power is equal to

When the alternative hypothesis makes a specific prediction, one should use

Why does "nonsignificance" not mean the same thing as "no effect" in null hypothesis significance testing?

At the beginning of a study, the researcher hypothesizes that there is no difference between the experimental and control groups. This is an example of a(n)

Which of the following is NOT a name for the probability of making a Type I error?

When would one use a one-tailed p value rather than a two-tailed p value? Why would one prefer to use a one-tailed p value in these situations?

____________ tells us the nonnull magnitude of the effect size that is supported by exactly the same amount of evidence as is the null value of the effect size.

Why are Type I and Type II errors characterized respectively as being errors of gullibility and blindness to a relationship?

What are the two common errors in thinking about effect sizes that the counternull statistic helps the researcher minimize? Is this the same as Type I and Type II errors?

How can a statistical power analysis be useful when planning an experimental study?

Which of the following refers to the probability of making a Type II error?

The counternull statistic is useful for minimizing which of the following common errors (in thinking about effect sizes)?

Why would one want to calculate the confidence interval associated with an observed effect size?

The initials "NHST" stand for

Behavioral Research and the Scientific Method

From Hunches to Testable Hypotheses

Ethical Considerations and Guidelines

Carpenter, S 1998 Content Analysis Project for Research Novices Teaching of Psychology

Methods for Looking Within Ourselves

Reliability and Validity in Measurement and Research

Randomized Experiments and Causal Inference

Nonrandomized Research and Causal Reasoning

Survey Research and Subject Recruitment

Summarizing the Data

Correlating Variables

The Comparison of Two Conditions

Comparisons of More Than Two Conditions

The Analysis of Frequency Tables

Filters

Exam 12: Understanding P Values and Effect Size Indicators

Mistakenly rejecting the null hypothesis when it is true and should not have been rejected is technically called a(n)

Power is equal to

When the alternative hypothesis makes a specific prediction, one should use

Why does "nonsignificance" not mean the same thing as "no effect" in null hypothesis significance testing?

At the beginning of a study, the researcher hypothesizes that there is no difference between the experimental and control groups. This is an example of a(n)

Which of the following is NOT a name for the probability of making a Type I error?

When would one use a one-tailed p value rather than a two-tailed p value? Why would one prefer to use a one-tailed p value in these situations?

____________ tells us the nonnull magnitude of the effect size that is supported by exactly the same amount of evidence as is the null value of the effect size.

Why are Type I and Type II errors characterized respectively as being errors of gullibility and blindness to a relationship?

What are the two common errors in thinking about effect sizes that the counternull statistic helps the researcher minimize? Is this the same as Type I and Type II errors?

How can a statistical power analysis be useful when planning an experimental study?

Which of the following refers to the probability of making a Type II error?

The counternull statistic is useful for minimizing which of the following common errors (in thinking about effect sizes)?

Why would one want to calculate the confidence interval associated with an observed effect size?

The initials "NHST" stand for

Behavioral Research and the Scientific Method

From Hunches to Testable Hypotheses

Ethical Considerations and Guidelines

Carpenter, S 1998 Content Analysis Project for Research Novices Teaching of Psychology

Methods for Looking Within Ourselves

Reliability and Validity in Measurement and Research

Randomized Experiments and Causal Inference

Nonrandomized Research and Causal Reasoning

Survey Research and Subject Recruitment

Summarizing the Data

Correlating Variables

The Comparison of Two Conditions

Comparisons of More Than Two Conditions

The Analysis of Frequency Tables

Filters