It's been a while since I was into this, but I think the problem of the bounds discussed in Myth 5 is that while the theory is fine, in practice the bounds are

It's been a while since I was into this, but I think the problem of the bounds discussed in Myth 5 is that while the theory is fine, in practice the bounds are big, and Bayesian methods converge faster. The discussion also doesn't seem to discuss incremental decision making where posterior(t-1) -> prior(t). I note that the discussion avoids the crazy stuff with frequentist stopping rules which is much more elegantly handled using Bayesian & decision theoretic methods. I agree that there are quite a few myths around frequentist methods as described in the article.