Graph Models
I owe you a post on the rhetoric of PGM's.
In a 1980's volume Connectionist Symbol Processing, Geoff Hinton drew a graphical model with convenient variable names and cute pictures. Eg rained yesterday → rains today. Not that "whatever A and B I stick into my graph shaped like this, it will make sense".
In chapter 19 of All of Statistics Larry Wasserman does the same thing.
iirc L S Paul's book on causality and Judea Pearl's do the same thing.
Deborah Mayo may or may not be guilty. (I'm guessing not.)
Elliott Sober Parsimony and Prediction draws a convenient looking curve. It hides the difficulties you would knock into if you considered parameterized families of curves.
You can't just conveniently title your variables. That is not an argument.
The structure itself needs to actually work for what you are saying it will. Be it Fourier, Markov, Lagrange, Fulton, etc.
