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The phrase "average difference from" is correct and usable in written English.
It can be used when discussing statistical measures or comparisons between data sets, particularly in contexts involving averages or deviations.
Example: "The average difference from the expected value indicates how much the results vary from what was anticipated."
Alternatives: "mean deviation from" or "typical difference from".
Exact(6)
A few of the algorithms (algorithms 18 21) differed substantially from original table values under all metrics considered, with an average difference from original table values of > 5°C/9°F for both metrics (Table 2).
The average difference from preoperative to intraoperative was 4.4° (−21° to 5°) for the inlet and 0.45° (−9° to 7°) for the outlet.
For each face we computed a second measure taking the difference from the average difference from the mean for each trait.
P values in bold test for an average difference from zero across all age groups.
This is visually evident in Figure 4 with the Q-Q plots for the function-constrained samples closer to the diagonal (left columns), and their average difference from the disease sets closer to zero (right column).
Other algorithms (algorithms 18 21; Table 2) differed substantially from original apparent temperature values as judged by both weighted and unweighted metrics for both tables, with an average difference from original table values of > 5°C/9°F for both metrics.
Similar(54)
BMQ scores were generally stable over time, with average differences from baseline within one point in either direction for all scales.
Clade 1 (red) and clade 2 (green) contain 147 and 113 elements each, and are more divergent, with 9.1% and 12.9% average differences from the consensus respectively.
The mean (± standard deviations) differences per participant showed similar results, with average differences from 0.100 (HUI2/HUI3) to −0.122 (SF-6D/HUI2) and 95% confidence intervals as great as −0465 to 0.417 (SF-6D/HUI2).
We hypothesize that there is no bias to the left or right side in each of these parameters, showing average difference values not significantly different from zero.
RESULTS: The global noise level agreed well with the phantom-based and clinical image-based noise measurements, with an average difference of 3.4%and4.7%7% from each of these measures, respectively.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com