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In an effort to reduce type 1 error from multiple comparisons, the P-value for significance was set at ≤0.01.
However, even though we performed many separate analyses the number of significant findings was limited, which may speak against major influence from type 1 error from multiple testing.
More detailed models will inevitably contain more component sub-models, with more parameters overall, which increases the possibility of accumulation of error from multiple sources.
A P-value below 0.005 was considered statistically significant after Bonferroni adjustment to control for type I error from multiple comparisons.
Also, to account for the added error from multiple imputation, we assumed a 10% higher sd (2.53 kg) than in the pilot study, which decreased the effect size and increased the needed sample size slightly.
Given the risk of increased Type I error from multiple comparisons (total tests of main variables of interest = 54), alpha was corrected from p < .01 to p < .0046 based on the procedure identified by Benjamini and Hochberg [ 26].
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This study concerns the training of a neural network in multiple stages considering minimization of errors from multiple data/pattern resources.
In addition, it is suggested validation in a larger number of samples for overcoming errors from multiple testing.
In other words, the contribution to VWM variability of recall errors from multiple items becomes more independent with age, although this effect is modest.
An error propagation method is used when we combine the standard errors from multiple sources without original data (e.g., the integration of standard errors from forward and inverse modeling methods).
In this way priority was given to the effect of treatment on those measures of most potential value first, reducing the risk of Type II errors from multiple testing.
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