Then you should take the Bayesian side, because Bayesians look at the data first, and they take their data as given rather than taking a null hypothesis as given. They don't just blindly go off and run a test (which assumes a particular prior implicitly that may be wildly inappropriate) and see what it says about the likelihood of their already observed data being generated by the test's assumed data generator.
But being a good bayesian makes you do exactly this. The process of describing priors makes it obvious you need to do a sensitivity analysis to check how much the prior is influencing the conclusions...
The people need to get over it. LW crowd is a group of people studying a pretty specific set of subjects, focused around a single website. It's typical for such a group to develop their own jargon and insider jokes, which may look weird from outside. It's normal.
"Good Bayesian" in that context just means being an able user of Bayesian statistics, not necessarily holding any particular philosophical belief about what they mean.
How can you evaluate the strength of your data without using statistics? You've created a catch-22.
I'll speculate you have some sort of meta-heuristic and only apply this catch-22 under those circumstances? E.g. this catch-22 only applies to weird and socially disapproved topics?
