What this calculator does
Mean squared error (MSE) measures how far a set of predictions is from the actual values, on average, by squaring each error before averaging. It is one of the most common ways to evaluate a forecasting model, regression line, or machine learning prediction.
How the calculation works
For each pair you enter, the calculator finds the error (actual minus predicted), squares it, and averages the squared errors across all pairs: MSE = (1/n) Σ(actual – predicted)². It also reports the root mean squared error (RMSE, the square root of MSE, in the same units as your data) and the mean absolute error (MAE, the average of the absolute errors).
Squaring the errors means MSE penalizes large mistakes more heavily than small ones, so a single big miss can raise it substantially even if most predictions are close. This tool is for general and educational statistical use.