Volume 3 • Issue 1 | March 2019
Reality-Based Probability & Statistics: Solving the Evidential Crisis
William M. Briggs
Artificial intelligence
Cause
Decision making
Evidence
Falsification
Machine learning
Model selection
Philosophy of probability
Prediction
Proper scores
P-values
Skill scores
Tests of stationarity
Verification
Abstract:
It is past time to abandon significance testing. In case there is any reluctance to embrace this decision, proofs against the validity of testing to make decisions or to identify cause are given. In their place, models should be cast in their reality-based, predictive form. This form can be used for model selection, observable predictions, or for explaining outcomes. Cause is the ultimate explanation; yet the understanding of cause in modeling is severely lacking. All models should undergo formal verification, where their predictions are tested against competitors and against reality.
JEL classification: C10