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We know that in statistics, the Type II error happens when the null hypothesis is false but fails to get rejected.
Type II error in this scenario will be when the researcher claim 65% of college students will graduate with debt is false but fails to be rejected.
A type II error is a statistical term used within the context of hypothesis testing that describes the error that occurs when one fails to reject a null hypothesis that is actually false. A type II error produces a false negative, also known as an error of omission.
For example, a test for a disease may report a negative result when the patient is infected. This is a type II error because we accept the conclusion of the test as negative, even though it is incorrect.
A Type II error can be contrasted with a type I error is the rejection of a true null hypothesis, whereas a type II error describes the error that occurs when one fails to reject a null hypothesis that is actually false. The error rejects the alternative hypothesis, even though it does not occur due to chance.
Given : The null hypothesis, , is: researchers claim that 65% of college students will graduate with debt.
Then , Type II error in this scenario will be when the researcher claim 65% of college students will graduate with debt is false but fails to be rejected.
Learn more about Type II error:
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