That's fair, it forces you to document your assumptions. But at some point as you drill down into assumptions, the trail will grow cold. The article even talks about inserting priors that are either subjective or some default function provided by the software.

A possible third approach is to create a hypothetical model and feed random data through it, to get an idea of the spread of the outcomes. Modeling doesn't stumble on conditional terms, and if you're unclear on an assumption, it won't run. The computer doesn't know whether it's a frequentist or a Bayesian, or something different from both.

I'm not a statistician. Whenever I need to do something with statistics, I always test my computation with random data.

In fact I wonder, if modeling had been possible since the birth of statistics, if we would even bother with things like elaborate formulas for statistical tests.