The correct answer is:

(Choice D) They conclude the new panel is more effective when it actually is not more effective.

Explanation:

A Type I error occurs when the null hypothesis is rejected when it is actually true. In this context, the null hypothesis is that the new solar panel is no more effective than the older model.

• A Type I error would mean that the quality control expert wrongly concludes that the new panel is more effective than the old one when, in fact, it is not.

So, the consequence of a Type I error here would be falsely concluding that the new panel is better when it isn't. This matches Choice D.

Would you like further clarification or details on hypothesis testing?

Here are five related questions for exploration:

1. What is a Type II error, and how does it differ from a Type I error?
2. How do you calculate the significance level (α) in hypothesis testing?
3. What are the implications of a Type I error in medical testing scenarios?
4. How would you interpret the p-value in the context of hypothesis testing for the solar panel?
5. What role does sample size play in minimizing the risk of a Type I or Type II error?

Tip: Understanding the difference between Type I and Type II errors is crucial in hypothesis testing, as each error has its own consequences depending on the context.