# Model Uncertainty, Data Mining and Statistical Inference

## Excerpts

Finally it does not seem helpful just to say that all models are wrong. The very word model implies simplification and idealization. The idea that complex physical, biological or sociological systems can be exactly described by a few formulae is patently absurd. The construction of idealized representations that capture important stable aspects of such systems is, however, a vital part of general scientific analysis and statistical models, especially substantive ones (Cox, 1990), do not seem essentially different from other kinds of model.

(D. R. Cox, Comments)

— Page 456

## Reference

Chris Chatfield

“Model Uncertainty, Data Mining and Statistical Inference”(1995) DOI: 10.2307/2983440

```
@Article{chatfield1995,
title = {Model Uncertainty, Data Mining and Statistical Inference},
volume = {158},
issn = {0964-1998},
url = {http://dx.doi.org/10.2307/2983440},
doi = {10.2307/2983440},
number = {3},
journal = {Journal of the Royal Statistical Society. Series A (Statistics in Society)},
publisher = {Oxford University Press (OUP)},
author = {Chatfield, Chris},
year = {1995},
pages = {419}
}
```