Regardless of your previous answers, let's say the conclusion you make at the end of the test is that the proportion of people favoring stricter gun laws has changed. In truth, the proportion of people favoring stricter gun laws has actually remained the same. What does this mean?

Group of answer choices

You made a Type I error.

You made a Type II error.


You made the correct decision.

This situation describes a **Type I error**.

### **Reason:**
- A **Type I error** occurs when you reject the null hypothesis (\(H_0\)) when it is actually true.  
- In this context:  
  - **Null Hypothesis (\(H_0\))**: The proportion of people favoring stricter gun laws has remained the same.  
  - **Alternative Hypothesis (\(H_a\))**: The proportion of people favoring stricter gun laws has changed.  

Since the truth is that the proportion has **remained the same**, but you concluded that it has **changed**, you mistakenly rejected the null hypothesis. This is a classic Type I error.

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**Correct Answer:** **"You made a Type I error."**

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Would you like additional details or examples about statistical errors?  

### **Related Questions:**
1. What is a Type II error and how does it differ from a Type I error?
2. How can you reduce the probability of making a Type I error in hypothesis testing?
3. What is the significance level (\(\alpha\)) and how does it relate to Type I errors?
4. Can you explain the power of a test and its connection to Type II errors?
5. How does sample size affect the likelihood of making Type I or Type II errors?

**Tip:**  
The lower the significance level (\(\alpha\)), the lower the chance of making a Type I error—but this increases the risk of a Type II error.

Regardless of your previous answers, let's say the conclusion you make at the end of the test is that the proportion of people favoring stricter gun laws has changed. In truth, the proportion of people favoring stricter gun laws has actually remained the same. What does this mean? Group of answer choices: You made a Type I error. You made a Type II error. You made the correct decision.

Explore the concept of a Type I error in hypothesis testing with an example about stricter gun laws. Learn how rejecting a true null hypothesis leads to an incorrect conclusion. This detailed solution explains Type I and Type II errors and their role in statistical decision-making.

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