What does a p-value of exactly 1.0000 mean? I have a table of pairwise comparisons using paired t-tests, which I produced using the following code in R: 
with(d_OAI, pairwise.t.test(OAI, Ven,p.adjust.method="bonferroni", paired=T))

Some of the p-values are significant (i.e. less than 0.05) and some are not. However, several of the p-values equal 1.00000. I assume this means the two things are definitely NOT significantly different, but is this R rounding up from 0.9999 or is a p-value of 1.000 meaningful?
 A: If the data are discrete it's possible to get an exact p-value of 1 on a paired t-test, when the mean difference is exactly 0.
Otherwise, yes, a value just less than 1 may be shown as 1 at some given number of significant figures.
A: @Cliff-ab already provided a right answer. In case you want to get further insight on the meaning of the obtained p-values, making a histogram out of them (before correcting for multiple testing) may be of help.
In particular, as nicely described by @david-robinson in http://varianceexplained.org/statistics/interpreting-pvalue-histogram/, p-values close to 1.0 may indicate that you have been applying a one-sided test when maybe you wanted a two-sided test or may be caused by missing values in your data, distorting the results of the test. Another option (as @Cliff-AB mentions) is the Bonferroni correction that you are applying, which seems the most plausible cause.
A: I'm not exactly familiar with the specific R functions, but if there's a Bonferroni correction, I believe that is likely to be the explanation. For example, suppose you tested two hypotheses and got unadjusted p = 0.6, 0.6. The simplistic Bonferroni adjustment would be 1.2, 1.2, but since these are not valid probabilities, it would truncate these to 1.0 and 1.0.
