Thread by @dailyzad

This paper by @philipbstark is a must read for anyone who builds and fits statistical models as it shows how most stat models in several disciplines are insanely disconnected from reality 1/n

There are so many excellent points and examples in the paper so the rest of this thread will be screenshots with some minor annotations, first I focus on general points in the paper and then discuss the model examples which start around tweet #20 2/n

Issue: Quantifauxcation and forcing complex phenomenon into simplistic numbers for the sake of rigor and comparisons 3/n

Issues with many “cost-benefit analyses” 4/n

Can something as complex as uncertainty always be represented by probability? 5/n

Some general remarks about frequency and subjective based interpretations of probability 6/n

General remarks about probability continued 7/n

Are coin tosses really random? 8/n

Can we trust peoples intuitions and perceptions about complex phenomenon and how that translates into components of the model? 9/n

A common issue in statistical modeling and interpretation, confusing rates with probabilities 10/n

What does random even mean? What is unpredictable? 11/n

How inferential outputs assume a random component is being modeled in order to be valid 12/n

The relationship between probabilistic models and science 13/n

Is it true in practice that with more data the prior (a modeling choice) will eventually be swamped? 14/n

If you model something as being random when it is not does the probabilistic interpretation seem sensible? 15/n

What about situations in which you create randomness and account for it? 16/n

Simulations are completely dependent on what you bake into them, they don’t reflect reality itself 17/n

The role of sensitivity analyses and uncertainty quantification 18/n

What if you started calculating inferential statistics for phenomenon that have no stochastic component? 19/n

Some extremely helpful practical examples in seismology: PHSA models that are based on metaphors instead of actual physics 20/n

Helpful example: issues with a model used to explain wind turbine interactions with birds resulting in a type III error 21/n

Helpful example: statistical tests employed in a randomized clinical trial 22/n

Helpful example: modeling whether women are more likely to be interrupted in academic talks and how the chosen model resulted in a type III error 23/n

Helpful example: the issue with all the models used in the many analysts, one dataset study and how this was also a type III error 24/n

Parallels between models and conspiracies 25/n

Some general remarks about models #statstwitter