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If the statistical model was obtained via a regression, then [[Regression validation#Analysis of residuals|regression-residual diagnostics]] exist and may be used; such diagnostics have been well studied.
=== Cross
{{See|Cross-validation (statistics)}}
Cross validation is a method of sampling that involves leaving some parts of the data out of the fitting process and then seeing whether those data that are left out are close or far away from where the model predicts they would be. What that means practically is that cross validation techniques fit the model many, many times with a portion of the data and compares each model fit to the portion it did not use. If the models very rarely describe the data that they were not trained on, then the model is probably wrong.
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