Small errors
pairs = 3, 2.5 -0.5, 0 2, 2 7, 8
Result: MSE 0.375
MSE Calculator
Mean squared error (MSE) is the average of the squared differences between actual and predicted values. Because errors are squared, large mistakes are penalized heavily. Paste one "actual, predicted" pair per line and get MSE, plus RMSE, MAE, and the number of pairs.
Enter your values and press Calculate — the result appears here.
Worked examples
Perfect predictions
pairs = 10, 10 20, 20 30, 30
Result: MSE 0
One large outlier
pairs = 1, 1 2, 2 3, 10
Result: MSE 16.67 — the outlier dominates
Frequently asked questions
What does MSE measure?
The average squared difference between predictions and reality. It is always ≥ 0, and 0 means perfect prediction. Squaring means an error of 10 contributes 100 times more than an error of 1, so MSE is sensitive to outliers.
What are the units of MSE?
Squared units of the target variable. If you predict prices in dollars, MSE is in dollars squared, which is hard to read — which is why RMSE (the square root) is often reported alongside it.
MSE or MAE?
MSE heavily penalizes large errors and rewards models that avoid them; MAE treats all errors equally and is robust to outliers. If outliers matter to your use case, prefer MSE; if you want a typical-error view, prefer MAE.
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