Question 1

Which of the following best describes the p-value of a test?

Accepted Answer

A) The p-value is the probability of getting our statistical table value of the test statistic if the null hypothesized value of the population parameter, were really true. 
B) The p-value is the probability of getting our calculated test statistic or more extreme if the null hypothesized value of the population parameter, were really true. 
C) The p-value is the probability of getting our calculated test statistic, or more extreme, if the null hypothesized value of the population parameter, were really false. 
D) None of these choices are correct. 
A) The p-value is the probability of getting our statistical table value of the test statistic if the null hypothesized value of the population parameter, were really true. 
B) The p-value is the probability of getting our calculated test statistic or more extreme if the null hypothesized value of the population parameter, were really true. 
C) The p-value is the probability of getting our calculated test statistic, or more extreme, if the null hypothesized value of the population parameter, were really false. 
D) None of these choices are correct.

Question 2

There is an inverse relationship between the probabilities of Type I and Type II errors.

Accepted Answer

A) True 
 B)False

Question 3

In testing the hypotheses H0: F1F1F1S1F1F1F10 = 75
HA: F1F1F1S1F1F1F10 < 75,
If the value of the Z test statistic equals -1.78, then the p-value is:

Accepted Answer

A) 0.0.375. 
B) 0.4625. 
C) 0.9625. 
D) 0.5375. 
A) 0.0.375. 
B) 0.4625. 
C) 0.9625. 
D) 0.5375.

Question 4

A Type II error is committed if we make:

Accepted Answer

A) a correct decision when the null hypothesis is false. 
B) correct decision when the null hypothesis is true. 
C) incorrect decision when the null hypothesis is false. 
D) incorrect decision when the null hypothesis is true. 
A) a correct decision when the null hypothesis is false. 
B) correct decision when the null hypothesis is true. 
C) incorrect decision when the null hypothesis is false. 
D) incorrect decision when the null hypothesis is true.

Which of the following best describes the p-value of a test?

There is an inverse relationship between the probabilities of Type I and Type II errors.

In testing the hypotheses H0:  = 75 HA:  < 75, If the value of the Z test statistic equals -1.78, then the p-value is:

A Type II error is committed if we make:

What Is Statistics

Types of Data, Data Collection and Sampling

Graphical Descriptive Techniques Nominal Data

Graphical Descriptive Techniques Numerical Data

Numerical Descriptive Measures

Probability

Random Variables and Discrete Probability Distributions

Continuous Probability Distributions

Statistical Inference and Sampling Distributions

Estimation: Describing a Single Population

Estimation: Comparing Two Populations

Hypothesis Testing: Comparing Two Populations

Additional Tests for Nominal Data: Chi-Squared Tests

Simple Linear Regression and Correlation

Multiple Regression

Time-Series Analysis and Forecasting

Index Numbers

Filters

Exam 12: Hypothesis Testing: Describing a Single Population

Which of the following best describes the p-value of a test?

There is an inverse relationship between the probabilities of Type I and Type II errors.

In testing the hypotheses H0:  = 75 HA:  < 75, If the value of the Z test statistic equals -1.78, then the p-value is:

A Type II error is committed if we make:

What Is Statistics

Types of Data, Data Collection and Sampling

Graphical Descriptive Techniques Nominal Data

Graphical Descriptive Techniques Numerical Data

Numerical Descriptive Measures

Probability

Random Variables and Discrete Probability Distributions

Continuous Probability Distributions

Statistical Inference and Sampling Distributions

Estimation: Describing a Single Population

Estimation: Comparing Two Populations

Hypothesis Testing: Comparing Two Populations

Additional Tests for Nominal Data: Chi-Squared Tests

Simple Linear Regression and Correlation

Multiple Regression

Time-Series Analysis and Forecasting

Index Numbers

Filters