Mean Squared Error (MSE)
Definition
The MSE evaluates prediction errors and weights large deviations particularly strongly.
The mean of the squared distances between prediction and observation.
Simply explained
Errors are measured and squared; large errors thus have a stronger impact.
Why it matters in practice
Helps realistically assess model quality, limits, and technical decisions.
Example from business
Possible example: A forecast error of two units contributes four to the squared error; an error of one unit contributes one.
Limits and pitfalls
- MSE has a squared unit and must not be read like the original measurement unit. Large outliers influence it particularly strongly.
Origin & history
MSE is closely related to the method of least squares and Gaussian error models.
Use in mid-sized companies
- Possible application: Large forecast errors are weighted more heavily by squaring.
Frequently asked questions
What does Mean Squared Error (MSE) mean?
The mean of the squared distances between prediction and observation.
Which mistakes should be avoided?
Reading the unit as the original unit or overlooking sensitivity to outliers.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: