All models are wrong
But some are useful (and that’s not the purpose anyway)
If you have ever taken a statistics course that deals with modeling or probabilistic forecasting, you may have heard the aphorism “all models are wrong.” This is generally attributed to George Box, who is noted in a 1976 paper in the Journal of the American Statistical Association, saying:
Since all models are wrong the scientist cannot obtain a “correct” one by excessive elaboration. On the contrary following William of Occam he should seek an economical description of natural phenomena. Just as the ability to devise simple but evocative models is the signature of the great scientist so overelaboration and overparameterization is often the mark of mediocrity. [1]
Box’s aphorism evolved two years later in a paper that was published in the proceedings of a 1978 statistics workshop, to include the contingency “all models are wrong, but some are useful.”
Now it would be very remarkable if any system existing in the real world could be exactly…
