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The problem presented involves a comparison between the p-value of a hypothesis test and the significance level (denoted by \(\alpha\)).

- The significance level (\(\alpha\)) is 1%, which is \(0.01\).
- The p-value from the test is \(0.004\).

In hypothesis testing, we reject the null hypothesis when the p-value is **less than or equal to** the significance level.

**Step-by-step analysis:**
1. The given p-value \(0.004\) is less than the significance level \(0.01\).
2. Therefore, \( \text{p-value} < \alpha \) is the correct comparison.

Thus, in the illustration provided:
- The first box is incorrect as the p-value is not equal to the significance level.
- The second box correctly shows that \( \text{p-value} < \alpha \).

Would you like more details on hypothesis testing and p-values? Here are five related questions:

1. What does it mean to reject the null hypothesis in this context?
2. How does the significance level affect the outcome of a hypothesis test?
3. What are Type I and Type II errors in hypothesis testing?
4. How is the p-value calculated in a hypothesis test?
5. What happens if the p-value equals the significance level exactly?

**Tip:** Always compare the p-value to the significance level to determine if the result is statistically significant.

A hypothesis test is conducted at the 1% significance level. The test yields a p-value of 0.004. Compare the level of significance and the p-value.

Explore how to compare the p-value (0.004) and the significance level (1%) in a hypothesis test. Learn the key concepts of hypothesis testing, including when to reject the null hypothesis based on the p-value.

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