I was a bit fuzzy on the general steps of hypothesis testing for a mean. The goal is to define a p value to describe the confidence you have in the mean being contained in the sample. First, you define a null hypothesis, for example, mu_0 = mean, and an alternative hypothesis that denies the null, for example, mu_ =/= mean. The null hypothesis will either be rejected or not be rejected. P values measure the evidence against the null hypothesis, so a small p-value (below the significance level) would tell us to reject the null. If we know the data is normally distributed, we will use the test statistic (Xbar - mu_0)/(s/sqrt(n)). We know this test statistic is a t-distribution with (n-1) degrees of freedom.
We would also find the p value, depending on whether we are testing for one or two - sided alternatives. Depending on this decision, the formula for finding a p value may look like: 2*P(T_(n-1) > abs(teststat)). I am still uncertain on the exact code needed for p value testing, but I understand the coding contains the number of degrees of freedom, whether it is one or two sided, and what the confidence level we are looking for will be.