In this case, the hypothesis test is set up as follows:

• Null hypothesis $H_0: p = 0.001$ (1% of Americans have seen a UFO).
• Alternative hypothesis $H_1: p < 0.001$ (less than 1% of Americans have seen a UFO).

Now, according to the problem:

• The true proportion of Americans who have seen a UFO is $p = 0.0015$ (0.15%).
• The test supports the skeptical researcher's claim, meaning that the test concludes $p < 0.001$.

Analysis:

• A Type I error occurs when the null hypothesis is true, but the test incorrectly rejects it.
• A Type II error occurs when the null hypothesis is false, but the test fails to reject it.
• A correct decision occurs when the test's conclusion aligns with the true proportion.

Here, the true proportion is $p = 0.0015$, which is slightly higher than 0.1%. Since the test incorrectly supports the claim that $p < 0.001$, it has failed to recognize that the true proportion is actually larger than the claimed 0.1%.

Thus, this situation reflects a Type I Error, because the test has incorrectly rejected a true null hypothesis.

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Would you like more details or have any questions?
Here are five related questions to explore:

1. What are the differences between Type I and Type II errors?
2. How can the significance level ($\alpha$) affect Type I errors?
3. What steps can be taken to reduce Type II errors in hypothesis testing?
4. Can a Type I error and Type II error occur in the same test?
5. How is statistical power related to Type II errors?

Tip: Lowering the significance level (α) reduces the likelihood of a Type I error but increases the risk of a Type II error. Balancing these is key in hypothesis testing.