I'm a stats-heavy computer scientist, but I'd argue the opposite: the ability to construct and evaluate a rigorous argument is the first thing lacking: one that plausibly analyzes the domain, recounts opposing arguments reasonably fairly, constructs counterarguments that are actually responsive to the opponents' arguments, and uses logical argumentation along with empirical evidence in a way that correctly supports its points.
This seems closer to "statistics versus logos" than "statistics versus rhetoric."
So on one side you run a T-test to see if the correlation is significant.
On the other side you have the ability to phrase it as a question in the same language as Aristotle.
So on one side you run a T-test to see if the correlation is significant.
This is an incredibly gross simplification of applying statistical theory. I suggest you pick up some texts on inference, Bayesian stats, and probability theory to learn how stats != "running t-tests".
He was clearly just picking one useful tool that stats provides, not stating that it was the entirety of statistics.
I'm not making any statement about whether I agree with him, but if you disagree you will have to claim that he is cherry picking one useful and one useless skill, not just say that there is more to stats than t-tests.
Statistics does fit into that, as one particular species of correctly using empirical evidence. But most freshmen students aren't even constructing coherent arguments in the broad sense; having a technical knowledge of statistical theorems isn't the main bottleneck. This is actually a common problem in my classes: technically-solid students who can't write a coherent essay-length or blog-post-length argument in favor of or against a point. The better students tended to have taken at least a Philosophy 101 course, which taught them how to write a basic "argument for or against a position" paper. Once you have that, there's a pretty good basis on which to expand the argumentation toolbox with statistical arguments.