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.