1. Rigorous (this one) - How to use frequentist techniques to define a very rigorous statistical procedure that - no matter how small the bias in the test - can give correct answers with very high probability (and, of course, support multiple looks at your statistics).
2. Fixed data - Your business has an upper limit on how much data it realistically can collect. How to design a statistical procedure that makes reasonably good decisions while remaining within that limit.
3. Simple test - How to use Bayes' Theorem to design very simple and straightforward test criteria, where every A/B test has a known cost up front.
4. Bayesian statistics - How a Bayesian approach compares to the previous ones. Where the sticking points are, what its advantages are.
5. Bayesian optimization - How a Bayesian approach can let you turn the problem of A/B testing into a pure optimization problem where your decision to stop is based on whether the cost of continuing the test exceeds your expected return from continuing it over some time horizon.
We'll see how far I get. As it goes on, they become tricker to write at the level that I'm aiming for. And it may be that people will be interested in something else. But I thought that the first two were important. If for no other reason than to give me something definitive to point people at who read Evan Miller's article and then were telling people to run A/B tests in a way that made no business sense.