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Multiple testing corrections can reduce type 1 errors, but they cannot solve the primary problem, that a single threshold in one analysis cannot distinguish between noise and signal of the same magnitude.
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Results in both cluster tested show that block correction can reduce significantly the computing time in the solution of very large linear systems.
The RMS error for the total residual was reduced from 0.68 to 0.58, as shown in Fig. 17(a), which corresponds to a demonstration that using station correction can reduce the expectation error by 15%.
At a tsunami frequency, the correction can reduce OBP fluctuations, due to rapid temperature changes, by as much as millimeters, and is especially effective for data at a station off Shikoku (MPG2) at which rapid temperature changes most frequently occur.
Additionally, online correction can reduce both systematic error and random error, whereas offline correction can only reduce systematic error [ 16, 17].
Although these results have not excluded the need for sodium correction, it is necessary to address whether sodium correction alone can reduce mortality.
The correction scheme can reduce the error in effective LAI measurements to within 2%.
This error correction step can reduce the number of erroneous reads and branches in the de Bruijn graph.
Our automatic methods for error mitigation cannot replace the need for additional sequencing, but they do allow substantial fractions of errors to be masked or eliminated at the cost of modest amounts of over-correction, and they can reduce the impact of error in downstream phylogenomic analyses.
Given the nature of this issue, it is unclear whether the correction of sodium can reduce the mortality risk.
Patients were able to continually verify the accuracy of their CHO calculations through improved postprandial glucose control as indicated by both SMBG data and fewer correction boluses, which can reduce the risk of insulin "stacking".
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