How well does a particular model work?

One form of model validation is to blindly test how well a fitted model predicts future, or otherwise unknown, outcomes.

Once we have those future, or previously unknown, data at hand we can retrospectively judge a model's ability to predict those data.

Essentially we are challenging if our 'best' model is a good-enough , or even useful, model.

Such verification presumes that the statistical error characteristics of the future or unknown data are the same as for the data used to estimate the model parameters.

Weather forecasters use retrospection to evaluate the accuracy of their predictions, then improve them.

Biologists use retrospection to evaluate the accuracy of fishing or hunting harvest forecasts or the accuracy of trends in population abundance, particularly for exploited or endangered species.

Retrospection is a powerful and conceptually simple diagnostic tool that relates directly to the purpose of the model.

It should be used whenever it is feasible.