Articles | Volume 15, issue 14
Review and perspective paper
 | Highlight paper
19 Jul 2022
Review and perspective paper | Highlight paper |  | 19 Jul 2022

Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not

Timothy O. Hodson


Total article views: 14,084 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
9,591 4,367 126 14,084 179 95
  • HTML: 9,591
  • PDF: 4,367
  • XML: 126
  • Total: 14,084
  • BibTeX: 179
  • EndNote: 95
Views and downloads (calculated since 11 Mar 2022)
Cumulative views and downloads (calculated since 11 Mar 2022)

Viewed (geographical distribution)

Total article views: 14,084 (including HTML, PDF, and XML) Thereof 13,040 with geography defined and 1,044 with unknown origin.
Country # Views %
  • 1


Latest update: 27 Nov 2023
Executive editor
This is a beautifully written exposition of the properties of two key statistics used in the evalution of models. Everyone working with models should read this paper.
Short summary
The task of evaluating competing models is fundamental to science. Models are evaluated based on an objective function, the choice of which ultimately influences what scientists learn from their observations. The mean absolute error (MAE) and root-mean-squared error (RMSE) are two such functions. Both are widely used, yet there remains enduring confusion over their use. This article reviews the theoretical justification behind their usage, as well as alternatives for when they are not suitable.