I found this 2009 JSA talk by Thad Tarpey to provide a useful explanation and commentary on the Box passage. He argues that if we regard models as approximations to the truth, we could just as easily call all models right.
Here’s the abstract:
Students of statistics are often introduced to George Box’s famous quote: “all models are wrong, some are useful.” In this talk I argue that this quote, although useful, is wrong. A different and more positive perspective is to acknowledge that a model is simply a means of extracting information of interest from data. The truth is infinitely complex and a model is merely an approximation to the truth. If the approximation is poor or misleading, then the model is useless. In this talk I give examples of correct models that are not true models. I illustrate how the notion of a “wrong” model can lead to wrong conclusions.