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The first one takes on an information theory approach, while the second one yields design criteria motivated by a physical interpretation of the error probability.
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We also recommend reporting MAE as it allows an intuitive interpretation of the errors.
For the interpretation of the measurement error, the largest limit of agreement (either upper or lower limit) is most relevant.
In this case the interpretation of the random error might be awkward as it could imply that a non-integer number of individuals would be needed to achieve the same precision as the standard hypothetical study.
Table 3 gives a clinically relevant thickness interpretation of the boundary errors, visualized in Fig. 7.
Thus, the biological interpretation of the bounded-error metrics is straightforward since they can be directly related to biological concepts instead of the mathematical construct of the FIM.
Hence, a true interpretation of the total percentage error is only possible if the precision of each method is reported separately.
A straightforward interpretation of the TD prediction error hypothesis would suggest that in this case (a) dopamine activity at the time of the predictive stimuli would scale with the probability of reward, and (b) on average over trials, the dopaminergic response after the stimulus and all the way to the time of the reward, should be zero.
While the interpretation of error in the value of x ′ β is straightforward in linear regression, this is more difficult in logistic regression and it then seems clearer to state bounds on the probabilities.
The water saturation calculated by saturation height function is consistent with that of the well log interpretation and the error is less than by other methods.
Interpretation of systematic error highlighted the need to augment pycnophylactic interpolation techniques with consideration of covariates of profit such as rainfall for better estimation in areas with high profit gradients.
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