What is the primary benefit of determining forecast accuracy using the statistical error function?

Determining forecast accuracy using the statistical error function primarily benefits organizations by identifying the most applicable statistical measures that can be used to evaluate and improve forecasting performance. By applying these error measures, such as Mean Absolute Error or Mean Squared Error, practitioners can assess how close their forecasts are to actual outcomes, thus enabling them to pinpoint specific areas of accuracy and inaccuracy.

This process not only highlights which statistical measures are most relevant based on the context and nature of the data but also helps in refining the forecasting approach. By understanding these applicable measures, companies can make informed decisions on which forecasting models to implement or adjust, ultimately leading to more reliable predictions and better planning outcomes.

The other options, while they may offer some benefits in forecasting, do not directly address the core advantage of utilizing statistical error functions in terms of improving forecast accuracy and model selection.