Before we do that, let's just define what our null hypothesis and our alternative hypothesis are going to be. Our null hypothesis can be that we don't meet the standards, that we just barely don't meet the standards, that the mean of our new engines is exactly 20 parts per million, and you essentially want the best possible value where we still don't meet, or the lowest possible value where we still don't meet the standard. Then our alternative hypothesis is no, we do meet the standard, that the true mean for our new engines is below 20 parts per million. To see if the data that we have is sufficient, what we're going to do is assume that this is true. Given that this is true, if we assume this is true, and the probability of this occurring, and the probability of getting a sample mean of that, is less than 1%, then we will reject the null hypothesis. We are going to reject our null hypothesis if the probability of getting a sample mean of 17.17, given the null hypothesis is true, is less than 1%. Notice, if we do it this way, there will be less than a 1% chance that we are making a type 1 error.