However, it is important to highlight assumptions (a), (b) and (c) have not been verified and are likely to be incorrect, and moreover that this estimate of the study level SMD and its standard error are sensitive to variation in these assumptions.
This study demonstrates that the magnitudes of the resulting numerical errors are sensitive to the form of the equations considered, and suggests an optimal form to minimize these errors.
Similarly, the presence of large saccades was linked to large RMS errors, but the relationship between RMS errors and the tracking dynamics is indirect because RMS errors are sensitive to a phase offset; that is, even a perfect synchrony with a constant phase would yield a large error value.
We also compared three different approaches to postcode validation (most strict, a balanced approach, most relaxed), in order to evaluate whether data linkage errors were sensitive to postcodes validation rules, ranging from most strict (postcode area, postcode district, space, postal sector, unit code) to most relaxed (spaces removed).
Er offers quantitative information on the magnitude of the error and is sensitive to systematic errors at the 10% level (which are of analytical interest).
We find that error-monitoring differs from the subsequent correct response, in the sense that the phase on the correct after an error (FEC) is sensitive to external feedback, whereas errors (FE) are not.
Our analysis explicitly accounts for three major experimental difficulties: (i) standard measurement error of thermocouples, (ii) uncertainty in thermocouple (probe) positions, an error which is sensitive to temperature gradients, and (iii) an uncontrolled inlet temperature profile.
The dopamine value prediction error response is sensitive to both the first and second moments of the predicted reward distribution at two seconds after the stimulus.
The prediction error response is sensitive to the time of the reward; a delayed reward induces a depression at its original time and an activation at its new time [ 24, 25].
This is especially so when using an error metric that is sensitive to event priors such as proportion of misclassifications.
