[brockf]

A quantile-based confidence interval from bootstrapping can yield a 100% confidence interval that does not contain 0, i.e., with 100% of cases positive/negative. But that does not (necessarily) mean that there is a 100% chance that the new version is better than the old one. Confidence intervals are not Bayesian credible intervals and cannot be treated as such. (That said, making some certain assumptions about the underlying model can in some times allow one to treat nonparametric bootstraps in such a way.)

[stevenwu]

Right. The author finds 100% of the time for his current dataset but makes a statement that implies some certainty or inference on future cases. Like taking 100 men, 100 women and finding that 100 randomly matched pairs had the man taller than the woman 100 times, and making the claim that there is a 100% chance that men are taller than women.

The more I type the more I realize how pedantic this is, but we're emphasized in stats to pay extra attention to the conclusions we draw from the data we analyze.