To answer this question, let’s clarify the meaning of Type I and Type II errors in hypothesis testing:

1. Type I Error (False Positive): This occurs when the null hypothesis ($H_0$) is true, but we reject it. In the context of this problem, this would mean:

   • We conclude that the medication is effective when, in reality, it is not.

2. Type II Error (False Negative): This occurs when the null hypothesis ($H_0$) is false, but we fail to reject it. In the context of this problem, this would mean:

   • We conclude that the medication is not effective when, in reality, it is effective.

Corresponding Answers:

• Type I Error: Concluding the medication is effective when it is not.
• Type II Error: Concluding the medication is not effective when it is.

Would you like me to provide further examples or details on hypothesis testing errors? Here are some related questions you may find helpful:

1. What is the practical impact of a Type I error in medical research?
2. How do we control the probability of a Type I error in hypothesis testing?
3. What factors influence the probability of a Type II error?
4. How is the power of a test related to Type II errors?
5. Can you explain how sample size impacts Type I and Type II errors?

Tip: Always ensure the consequences of both errors are evaluated in the context of the real-world problem to determine the balance between Type I and Type II error probabilities.