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Some experts have argued that RMSD is less reliable than Relative Absolute Error.

In economics, the RMSD is used to determine whether an economic model fits economic indicators.

In structure based drug design, the RMSD is a measure of the difference between a crystal conformation of the ligand conformation and a docking prediction.

In bioinformatics, the root-mean-square deviation of atomic positions is the measure of the average distance between the atoms of superimposed proteins.

In meteorology, to see how effectively a mathematical model predicts the behavior of the atmosphere.

Furthermore, each error influences MAE in direct proportion to the absolute value of the error, which is not the case for RMSD.

MAE is fundamentally easier to understand than the square root of the average of squared errors. MAE is the average of the absolute values of the errors. MAE possesses advantages in interpretability over RMSD. Some researchers have recommended the use of the Mean Absolute Error (MAE) instead of the Root Mean Square Deviation. The RMSD of an estimator θ ^ Mean absolute error

Consequently, RMSD is sensitive to outliers.

The effect of each error on RMSD is proportional to the size of the squared error thus larger errors have a disproportionately large effect on RMSD. RMSD is the square root of the average of squared errors. However, comparisons across different types of data would be invalid because the measure is dependent on the scale of the numbers used. In general, a lower RMSD is better than a higher one. RMSD is always non-negative, and a value of 0 (almost never achieved in practice) would indicate a perfect fit to the data. RMSD is a measure of accuracy, to compare forecasting errors of different models for a particular dataset and not between datasets, as it is scale-dependent. The RMSD serves to aggregate the magnitudes of the errors in predictions for various data points into a single measure of predictive power. These deviations are called residuals when the calculations are performed over the data sample that was used for estimation and are called errors (or prediction errors) when computed out-of-sample. The RMSD represents the square root of the second sample moment of the differences between predicted values and observed values or the quadratic mean of these differences. The root-mean-square deviation ( RMSD) or root-mean-square error ( RMSE) is a frequently used measure of the differences between values (sample or population values) predicted by a model or an estimator and the values observed.