Statistical tests of a model's forecast performance are commonly conducted by splitting a given data set into an in-sample period, used for the initial parameter estimation and model selection, and an out-of-sample period, used to evaluate forecasting performance.
Empirical evidence based on out-of-sample forecast performance is generally considered more trustworthy than evidence based on in-sample performance, which can be more sensitive to outliers and data mining. Out-of-sample forecasts also better reflect the information available to the forecaster in "real time".