Well, I'll put it this way - if we take as our null hypothesis 'these 95% CIs really did have 95% coverage', would the observed coverage-rate have p<0.05? If it did, would you or him resort to 'No True Scotsman' again?
(A hint as to the answer: just a few non-coverages drive the null down to extremely low levels - think about multiplying 0.05 by 0.05...)
Yeah, I still think you're talking past one another. Wasserman's point is that something being a 95% confidence interval deductively entails that it has the relevant kind of frequentist coverage. That can no more fail to be true than 2+2 can stop being 4. The null, then, ought to be simply that these are really 95% confidence intervals, and the data then tell against that null by undermining a logical consequence of the null. The data might be excellent evidence that these aren't 95% confidence intervals. Of course, figuring out exactly why they aren't is ...
http://xkcd.com/1132/
Is this a fair representation of frequentists versus bayesians? I feel like every time the topic comes up, 'Bayesian statistics' is an applause light for me, and I'm not sure why I'm supposed to be applauding.