A drug considered unsafe if more than 5% of the patients develop serious side effects. In order to study the safety of a new drug, a Company tests the following null and alternative hypotheses. H :T <0.05 H¸ :n > 0.05· Discuss the type I and Type II error in this situation. Is this a good test? Explain.

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**Null and Alternative Hypotheses for Drug Safety Testing**

3. A drug is considered unsafe if more than 5% of the patients develop serious side effects. In order to study the safety of a new drug, a company tests the following null and alternative hypotheses:

- \( H_0 : \pi \leq 0.05 \)

- \( H_a : \pi > 0.05 \)

Discuss the Type I and Type II errors in this situation. Is this a good test? Explain.

**Type I Error (False Positive):** Occurs if we reject the null hypothesis \( H_0 \) when it is actually true. This would mean concluding that more than 5% of patients experience serious side effects when in fact, they do not.

**Type II Error (False Negative):** Happens if we fail to reject the null hypothesis \( H_0 \) when the alternative hypothesis \( H_a \) is true. This would imply mistakenly concluding that the drug is safe (5% or fewer serious side effects), even though more than 5% of patients are affected.

**Evaluation of the Test:**
To determine if this is a good test, consider the consequences and likelihood of Type I and Type II errors. Factors such as sample size, significance level, and power of the test must be assessed. The test is effective if it accurately identifies unsafe drugs while minimizing both error types.
Transcribed Image Text:**Null and Alternative Hypotheses for Drug Safety Testing** 3. A drug is considered unsafe if more than 5% of the patients develop serious side effects. In order to study the safety of a new drug, a company tests the following null and alternative hypotheses: - \( H_0 : \pi \leq 0.05 \) - \( H_a : \pi > 0.05 \) Discuss the Type I and Type II errors in this situation. Is this a good test? Explain. **Type I Error (False Positive):** Occurs if we reject the null hypothesis \( H_0 \) when it is actually true. This would mean concluding that more than 5% of patients experience serious side effects when in fact, they do not. **Type II Error (False Negative):** Happens if we fail to reject the null hypothesis \( H_0 \) when the alternative hypothesis \( H_a \) is true. This would imply mistakenly concluding that the drug is safe (5% or fewer serious side effects), even though more than 5% of patients are affected. **Evaluation of the Test:** To determine if this is a good test, consider the consequences and likelihood of Type I and Type II errors. Factors such as sample size, significance level, and power of the test must be assessed. The test is effective if it accurately identifies unsafe drugs while minimizing both error types.
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