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Analysis has shown a pronounced order effect, showing a first sample effect overestimation.
Fig. 3: Diagnostic ability of imaging predictors to anticipate depression recovery: leave-one-out cross-validation (solid line, CV, n − 1 patients) vs. full sample effect size estimations (dashed line, n patients).
The sample effect size, non-parametric statistics, or L2-norm can be used to graphically compare samples by Multidimensional Scaling (MDS).
Unfortunately, non-parametric tests do not have well defined sample effect sizes and so it is not possible to use them as an absolute point of comparison that is independent of sample size.
As measurements are likely to be affected by sediment turbidity and color, a variation in initial light measurement has been here suggested, in order to consider the sample effect at all time readings during the test.
Cohen's measure of sample effect size for comparing two sample means.
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Data was analyzed using a mixed linear model panelist, order and treatment (sample) effects.
To alleviate the finite sample effects, the forward backward averaging technique is introduced to enhance system performance.
In total, there is no reason to assume that the documented patient preferences will be biased substantially by sample effects.
Sample blanks were used to control for independent sample effects.
Some of the present findings may reflect sample effects in a single institution.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com