Question

In: Statistics and Probability

Two quality control experts want to test the null hypothesis that a new solar panel is...

Two quality control experts want to test the null hypothesis that a new solar panel is no more effective than an older model. What is the consequence of a Type I error in this context?

Group of answer choices

concluding the new panels are no more effective when in fact they are.

concluding the new panels are more effective when in fact they are not.

it is not possible to make a Type I error in this case; whether the new panels are no more effective depends on scientific evidence.

concluding the new panels are more effective when in fact they are.

concluding the new panels are no more effective when in fact they are not.

AND

What is the consequence of a Type II error in the context of the previous question?

Group of answer choices

it is not possible to make a Type II error in this case; whether the new panels are no more effective depends on scientific evidence.

concluding the new panels are more effective when in fact they are not.

concluding the new panels are no more effective when in fact they are.

concluding the new panels are no more effective when in fact they are not.

concluding the new panels are more effective when in fact they are.

Expert Solution

Null hypothesis : New solar panel is no more effective than an older model

The null hypothesis is true means that new solar panel is no more effective than an older model .

On the basis of the test either we accept or reject the null hypothesis

Type I error is the error of rejecting the null hypothesis when it is true .

That means on the basis of the test we reject the null hypothesis when it is true and conclude that new solar panel is more effective than an older model when in reality new solar panel is no more effective than an older model .

Thus , consequences of type I error is

Concluding new panels are more effective when in fact they are not .

Type II error is the error of accepting the null hypothesis when it is false .

That means on the basis of the test we fail to reject the null hypothesis when it is false and conclude that new solar panel is no more effective than an older model when in reality new solar panel is more effective than an older model .

Thus , consequences of type II error is

Concluding new panels are no more effective when in fact they are .

Note :For better understanding decision table is given below :

		Decision
		Reject H0	Accept H0
Actual	H0 True	Type I error	Correct decision
Scenario	H0 False	Correct Decision	Type II error

orchestra answered 1 year ago

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