Which of the following is true about forecasting error metrics?

• A. A lower MAPE indicates worse accuracy.
• B. RMSE is always lower than MAPE.
• C. The squared penalty in RMSE means larger errors are heavily punished.
• D. MAPE and RMSE cannot be compared across items of different scales.

Answer: (C)

Forecasting error metrics measure how far predictions miss the actual values, and RMSE in particular uses a squared penalty. Because errors are squared before averaging and then the root is taken, large mistakes become disproportionately influential, so bigger misses are heavily punished. That emphasis on large errors is why this statement is true: the squared term in RMSE makes the metric especially sensitive to outliers and big deviations. The other choices don’t hold: a lower MAPE actually indicates better accuracy, RMSE is not always lower than MAPE since they are on different scales and not directly comparable, and while MAPE is a percentage that can be compared across items with different scales, RMSE cannot be directly compared across scales without normalization.