l. Parametric tests (such as t or ANOVA) differ from nonparametric tests (such as chi-square) primarily in terms of the assumption they require and the data they use. Explain the differences.

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Answer 1

Textbook Question

Chapter 17, Problem 1P

l. Parametric tests (such as t or ANOVA) differ from nonparametric tests (such as chi-square) primarily in terms of the assumption they require and the data they use. Explain the differences.

Expert Solution & Answer

To determine

The differences between parametric and non-parametric tests.

Answer to Problem 1P

Solution:

Non Parametric tests	Parametric tests
No information about the population is available.	Information about the population is completely known.
No assumptions are made regarding the population.	Specific assumptions are made regarding the population.
The null hypothesis is free from parameters.	The null hypothesis is made on parameters of population distribution.

Explanation of Solution

The basic difference between parametric and non-parametric tests are the assumptions associated with non-parametric tests. They are:

The sample observations are independent.
The variable under study is continuous.
The probability density function is continuous.
Lower order moments exist.
The non-parametric tests do not require the population to have a normal distribution.

Conclusion:

These are the assumptions for non-parametric tests which are fewer and much weaker than those associated with parametric tests.

Justification:

This is why the non-parametric tests can be performed on data measured on any scale.

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Students have asked these similar questions

What parametric test is used when you want to compare more than two independent samples? a. Chi-square b. Independent t-test c. z-test d. one-way ANOVA

Find the area and normal curve Less than z = -2.11

A researcher was interested in whether head worn displays were better than tablet displays for assisting rst responders with their tasks. He had 17 paramedics use either a head worn display (n = 9) or a tablet display (n = 8) during a simulated medical emergency. Each paramedic’s subjective workload score was measured out of 20 (where 20 indicated the highest workload). The data did not conform to the assumptions of a parametric test. a. What is the rank sum for the Head worn display group? a. 80 b. 73 c. 71 d. 82 b. What is the WS? a. 71 b. 80 c. 73 d. 82 c. What critical value of WS do you compare your obtained value to? a. 53 b. 54 c. 49 d. 51

Answer 2

Textbook Question

Chapter 17, Problem 1P

l. Parametric tests (such as t or ANOVA) differ from nonparametric tests (such as chi-square) primarily in terms of the assumption they require and the data they use. Explain the differences.

Expert Solution & Answer

To determine

The differences between parametric and non-parametric tests.

Answer to Problem 1P

Solution:

Non Parametric tests	Parametric tests
No information about the population is available.	Information about the population is completely known.
No assumptions are made regarding the population.	Specific assumptions are made regarding the population.
The null hypothesis is free from parameters.	The null hypothesis is made on parameters of population distribution.

Explanation of Solution

The basic difference between parametric and non-parametric tests are the assumptions associated with non-parametric tests. They are:

The sample observations are independent.
The variable under study is continuous.
The probability density function is continuous.
Lower order moments exist.
The non-parametric tests do not require the population to have a normal distribution.

Conclusion:

These are the assumptions for non-parametric tests which are fewer and much weaker than those associated with parametric tests.

Justification:

This is why the non-parametric tests can be performed on data measured on any scale.

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Subscribe now to access step-by-step solutions to millions of textbook problems written by subject matter experts!

Answer 3

Textbook Question

Chapter 17, Problem 1P

l. Parametric tests (such as t or ANOVA) differ from nonparametric tests (such as chi-square) primarily in terms of the assumption they require and the data they use. Explain the differences.

Expert Solution & Answer

To determine

The differences between parametric and non-parametric tests.

Answer to Problem 1P

Solution:

Non Parametric tests	Parametric tests
No information about the population is available.	Information about the population is completely known.
No assumptions are made regarding the population.	Specific assumptions are made regarding the population.
The null hypothesis is free from parameters.	The null hypothesis is made on parameters of population distribution.

Explanation of Solution

The basic difference between parametric and non-parametric tests are the assumptions associated with non-parametric tests. They are:

The sample observations are independent.
The variable under study is continuous.
The probability density function is continuous.
Lower order moments exist.
The non-parametric tests do not require the population to have a normal distribution.

Conclusion:

These are the assumptions for non-parametric tests which are fewer and much weaker than those associated with parametric tests.

Justification:

This is why the non-parametric tests can be performed on data measured on any scale.

Want to see more full solutions like this?

Subscribe now to access step-by-step solutions to millions of textbook problems written by subject matter experts!

Answer 4

Textbook Question

Chapter 17, Problem 1P

l. Parametric tests (such as t or ANOVA) differ from nonparametric tests (such as chi-square) primarily in terms of the assumption they require and the data they use. Explain the differences.

Expert Solution & Answer

To determine

The differences between parametric and non-parametric tests.

Answer to Problem 1P

Solution:

Non Parametric tests	Parametric tests
No information about the population is available.	Information about the population is completely known.
No assumptions are made regarding the population.	Specific assumptions are made regarding the population.
The null hypothesis is free from parameters.	The null hypothesis is made on parameters of population distribution.

Explanation of Solution

The basic difference between parametric and non-parametric tests are the assumptions associated with non-parametric tests. They are:

The sample observations are independent.
The variable under study is continuous.
The probability density function is continuous.
Lower order moments exist.
The non-parametric tests do not require the population to have a normal distribution.

Conclusion:

These are the assumptions for non-parametric tests which are fewer and much weaker than those associated with parametric tests.

Justification:

This is why the non-parametric tests can be performed on data measured on any scale.

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Subscribe now to access step-by-step solutions to millions of textbook problems written by subject matter experts!

Answer 5

Textbook Question

Answer 6

Textbook Question

Answer 7

Expert Solution & Answer

To determine

The differences between parametric and non-parametric tests.

Answer to Problem 1P

Solution:

Non Parametric tests	Parametric tests
No information about the population is available.	Information about the population is completely known.
No assumptions are made regarding the population.	Specific assumptions are made regarding the population.
The null hypothesis is free from parameters.	The null hypothesis is made on parameters of population distribution.

Explanation of Solution

The basic difference between parametric and non-parametric tests are the assumptions associated with non-parametric tests. They are:

The sample observations are independent.
The variable under study is continuous.
The probability density function is continuous.
Lower order moments exist.
The non-parametric tests do not require the population to have a normal distribution.

Conclusion:

These are the assumptions for non-parametric tests which are fewer and much weaker than those associated with parametric tests.

Justification:

This is why the non-parametric tests can be performed on data measured on any scale.

Answer 8

Expert Solution & Answer

To determine

The differences between parametric and non-parametric tests.

Answer to Problem 1P

Solution:

Non Parametric tests	Parametric tests
No information about the population is available.	Information about the population is completely known.
No assumptions are made regarding the population.	Specific assumptions are made regarding the population.
The null hypothesis is free from parameters.	The null hypothesis is made on parameters of population distribution.

Explanation of Solution

The basic difference between parametric and non-parametric tests are the assumptions associated with non-parametric tests. They are:

The sample observations are independent.
The variable under study is continuous.
The probability density function is continuous.
Lower order moments exist.
The non-parametric tests do not require the population to have a normal distribution.

Conclusion:

These are the assumptions for non-parametric tests which are fewer and much weaker than those associated with parametric tests.

Justification:

This is why the non-parametric tests can be performed on data measured on any scale.

Answer 9

Expert Solution & Answer

Answer 10

Expert Solution & Answer

Answer 11

To determine

The differences between parametric and non-parametric tests.

Answer 12

Answer to Problem 1P

Solution:

Non Parametric tests	Parametric tests
No information about the population is available.	Information about the population is completely known.
No assumptions are made regarding the population.	Specific assumptions are made regarding the population.
The null hypothesis is free from parameters.	The null hypothesis is made on parameters of population distribution.

Answer 13

Explanation of Solution

The basic difference between parametric and non-parametric tests are the assumptions associated with non-parametric tests. They are:

The sample observations are independent.
The variable under study is continuous.
The probability density function is continuous.
Lower order moments exist.
The non-parametric tests do not require the population to have a normal distribution.

Conclusion:

These are the assumptions for non-parametric tests which are fewer and much weaker than those associated with parametric tests.

Justification:

This is why the non-parametric tests can be performed on data measured on any scale.

Answer 14

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l. Parametric tests (such as t or ANOVA) differ from nonparametric tests (such as chi-square) primarily in terms of the assumption they require and the data they use. Explain the differences.

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Answer to Problem 1P

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