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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-64', Anonymous Referee #1, 08 Apr 2022
    • AC1: 'Reply on RC1', Timothy Hodson, 12 Apr 2022
    • AC2: 'Reply on RC1 (Ammendment)', Timothy Hodson, 15 Apr 2022
  • RC2: 'Comment on gmd-2022-64', Anonymous Referee #2, 21 Apr 2022
    • AC3: 'Reply on RC2', Timothy Hodson, 26 Apr 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Timothy Hodson on behalf of the Authors (18 May 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (18 May 2022) by Riccardo Farneti
RR by Anonymous Referee #2 (01 Jun 2022)
ED: Publish subject to minor revisions (review by editor) (03 Jun 2022) by Riccardo Farneti
AR by Timothy Hodson on behalf of the Authors (17 Jun 2022)  Author's response   Author's tracked changes 
EF by Una Miškovic (21 Jun 2022)  Manuscript 
ED: Publish as is (22 Jun 2022) by Riccardo Farneti
ED: Publish as is (23 Jun 2022) by David Ham (Executive editor)
AR by Timothy Hodson on behalf of the Authors (23 Jun 2022)
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.