our example of computer program upgrades for faster and more accurate printing processes and procedures is a good illustration of how the null and alternative hypotheses work in practice. In this case, the null hypothesis would state that there is no significant difference in printing processes and procedures before and after the upgrade, and that the machine is already operating at peak capacity. The alternative hypothesis, on the other hand, would be that the upgrade results in less errors, more precision, and fewer margins of error, leading to more efficient printing processes and procedures. To test this hypothesis, you could gather data on the printing processes and procedures before and after the upgrade, such as error rates, printing speed, and accuracy. By comparing the data, you could determine whether the upgrade resulted in a statistically significant improvement in performance.

Having written that, I have a question for you: A random sample of 16 ATM transactions shows the mean transaction time of 2.8 minutes with a standard deviation of 1.2 minutes. You want to test the significance of the true mean by using a t-test. What is the decision rule in this case?