What does MSE represent?

• A. It magnifies the errors by squaring each one before adding them up and dividing by the number of forecast periods.
• B. It averages absolute errors
• C. It computes the maximum error
• D. It uses percentage errors

Answer: (A)

Mean Squared Error measures how far predictions are from actual values, on average, by using squared deviations. For each forecast, you take the error (forecast minus actual), square it, sum all those squared errors, and then divide by how many forecasts you have. Squaring ensures all errors contribute positively and it gives more weight to larger mistakes, which is useful when you want to heavily penalize big deviations and drive optimization toward reducing them. That combination—squaring the errors and averaging them—defines MSE. The other options describe different metrics: averaging absolute errors corresponds to Mean Absolute Error, taking the maximum error is the worst-case error, and using percentage errors is Mean Absolute Percentage Error.