JonnyKay

Kyra Mosby, M.D., has a patient who is complaining of severe abdominal pain. Based on an examination and the results from laboratory tests, Mosby states the following diagnosis hypothesis: Ho: Appendicitis, HA: Not Appendicitis. Dr. Mosby removes the patient’s appendix and the patient still complains of pain. Subsequent tests show that the gall bladder was causing the problem. By taking out the patient’s appendix, Dr. Mosby:

A) made a Type II error.

B) is correct.

C) made a Type I error.

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This statement is an example of a Type II error, which occurs when you fail to reject a hypothesis when it is actually false (also known as the power of the test).
The other statements are incorrect. A Type I error is the rejection of a hypothesis when it is actually true (also known as the significance level of the test).

JonnyKay

Which of the following statements about hypothesis testing is least accurate?

A) The null hypothesis is a statement about the value of a population parameter.

B) If the alternative hypothesis is Ha: µ > µ0, a two-tailed test is appropriate.

C) A Type II error is failing to reject a false null hypothesis.

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The hypotheses are always stated in terms of a population parameter. Type I and Type II are the two types of errors you can make – reject a null hypothesis that is true or fail to reject a null hypothesis that is false. The alternative may be one-sided (in which case a > or < sign is used) or two-sided (in which case a ≠ is used).

JonnyKay

Which of the following statements about hypothesis testing is most accurate? A Type I error is the probability of:

A) failing to reject a false hypothesis.

B) rejecting a true alternative hypothesis.

C) rejecting a true null hypothesis.

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The Type I error is the error of rejecting the null hypothesis when, in fact, the null is true.

JonnyKay

Which of the following statements about hypothesis testing is least accurate?

A) A Type I error is the probability of rejecting the null hypothesis when the null hypothesis is false.

B) The significance level is the probability of making a Type I error.

C) A Type II error is the probability of failing to reject a null hypothesis that is not true.

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

JonnyKay

John Jenkins, CFA, is performing a study on the behavior of the mean P/E ratio for a sample of small-cap companies. Which of the following statements is most accurate?

A) One minus the confidence level of the test represents the probability of making a Type II error.

B) The significance level of the test represents the probability of making a Type I error.

C) A Type I error represents the failure to reject the null hypothesis when it is, in truth, false.

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A Type I error is the rejection of the null when the null is actually true. The significance level of the test (alpha) (which is one minus the confidence level) is the probability of making a Type I error. A Type II error is the failure to reject the null when it is actually false.

JonnyKay

A Type II error:

A) fails to reject a false null hypothesis.

B) fails to reject a true null hypothesis.

C) rejects a true null hypothesis.

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A Type II error is defined as accepting the null hypothesis when it is actually false. The chance of making a Type II error is called beta risk.

JonnyKay

If we fail to reject the null hypothesis when it is false, what type of error has occured?

A) Type II.

B) Type III.

C) Type I.

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A Type II error is defined as failing to reject the null hypothesis when it is actually false.

JonnyKay

Which of the following statements regarding hypothesis testing is least accurate?

A) The significance level is the risk of making a type I error.

B) A type I error is acceptance of a hypothesis that is actually false.

C) A type II error is the acceptance of a hypothesis that is actually false.

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A type I error is the rejection of a hypothesis that is actually true.

JonnyKay

A Type I error:

A) rejects a false null hypothesis.

B) fails to reject a false null hypothesis.

C) rejects a true null hypothesis.

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A Type I Error is defined as rejecting the null hypothesis when it is actually true. The probability of committing a Type I error is the significance level or alpha risk.

JonnyKay

Which of the following statements regarding Type I and Type II errors is most accurate?

A) A Type I error is rejecting the null hypothesis when it is actually true.

B) A Type I error is failing to reject the null hypothesis when it is actually false.

C) A Type II error is rejecting the alternative hypothesis when it is actually true.

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A Type I Error is defined as rejecting the null hypothesis when it is actually true. The probability of committing a Type I error is the risk level or alpha risk.