The hypotheses for this statistical test are:

• Null Hypothesis ($H_0$): There is no difference in the average number of home runs at home and on the road.
• Alternative Hypothesis ($H_a$): The Rockies hit more home runs at home than on the road.

The p-value is reported as 0.00, which is significantly below any common significance level (e.g., $\alpha = 0.05$, $\alpha = 0.01$).

Conclusion

Since the p-value is extremely small (close to 0), you reject the null hypothesis ($H_0$) in favor of the alternative hypothesis ($H_a$). This means you have sufficient evidence to conclude that the Rockies hit more home runs at home than on the road.

Type of Error

If your conclusion is in error, it would be a Type I Error. A Type I error occurs when you incorrectly reject a true null hypothesis. In this context, it would mean that you concluded the Rockies hit more home runs at home than on the road, when in reality, there is no difference.

Follow-Up Questions

1. What is the significance level ($\alpha$) used for this test, and how does it compare to the p-value?
2. Can external factors (e.g., weather, stadium design) explain the observed difference in home runs?
3. How was the data for home and road games collected, and is it free from bias?
4. What statistical test was performed, and does it assume normality or other conditions?
5. How could a Type I error impact future decisions based on this conclusion?

Tip

Always verify the assumptions of the statistical test used (e.g., independence, normality, equal variances) to ensure the validity of your results.