“Robustness in the Strategy of Scientific Model Building”, George E. P. Box1979 (decision theory)⁠:

Robustness may be defined as the property of a procedure which renders the answers it gives insensitive to departures, of a kind which occur in practice, from ideal assumptions. Since assumptions imply some kind of scientific model, I believe that it is necessary to look at the process of scientific modeling itself to understand the nature of and the need for robust procedures.

Against such a view it might be urged that some useful robust procedures have been derived empirically without an explicitly stated model. However, an empirical procedure implies some unstated model and there is often great virtue in bringing into the open the kind of assumptions that lead to useful methods.

The need for robust methods seems to be intimately mixed up with the need for simple models. This we now discuss.

ALL MODELS ARE WRONG BUT SOME ARE USEFUL.

…For such a model there is no need to ask the question “Is the model true?”. If “truth” is to be the “whole truth” the answer must be “No”. The only question of interest is “Is the model illuminating and useful?”.

See Also:

• Science and Statistics

• How Should We Critique Research?

• How did software get so reliable without proof?

• When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis

• When the Numbers Do Not Add Up: The Practical Limits of Stochastologicals for Soft Psychology