Statistician (and brand new PhD student) Jerzy Wieczorek explains the usefulness of a master's degree in statistics.
There's a huge difference between undergraduate Stats 101 (apply a few standard procedures to nice clean datasets) and real data analysis work (figure out how to clean the data and modify your procedures to the messy context in front of you). So a masters-level mathematical/theoretical stats course, where you learn to prove which estimators have desirable properties or to derive tests that are appropriate in a given situation, is invaluable when you run into non-standard problems. The masters degree will also expose you to many techniques that you probably didn't cover as an undergrad: designing good experiments, computer-intensive methods like the bootstrap, special-use techniques like time series or spatial statistics, other inference philosophies like Bayesian statistics, etc.
Of course Jerzy and me are slightly biased. Saying a master's degree in statistics isn't worthwhile is like saying we wasted our time, but if you really want to learn data — whether it's for analysis, visualization, journalism, or whatever — statistics helps you get there.
And whereas the PhD route takes a certain type of person, most master's degrees take only two years to finish, and your analysis skills increase exponentially compared to that of an undergrad. Graduate statistics is also way more interesting, because you focus more on practical usage and less on hypothesis tests.