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Human data showed a remarkable degree of variability which might be due to between-donor variation or to unrecognized, subclinical affections of some blood donors.
The discrepancy between the number of cases per cluster and the proportion of cases in clusters may be due to between-family variation in τ.
28 The different conclusions could be due to between-study methodological differences, for example, in the chosen measures of distress and definitions of pregnancy (eg, serum positive test, clinical pregnancy or live birth).
The AMOVA of the control region sequences revealed that most of the variation (90%) is due to between-population differences (within population difference only 10%, p<0.0001).
The majority of the variance in responses to this subscale was due to between-person differences.
I was used to evaluate the percentage of total variation that was due to between-study heterogeneity.
This allowed us to compare variations in model estimates that were due to "between-source" versus "within-source" (i.e., variation due to different analyses).
A positive value of, for instance, 0.60 indicates that 60% of the variation is due to between-subject and 40% due to within-subject variance.
A positive value, for instance, of 0.60 indicates that 60% of the variation is due to between-subject and 40% due to within-subject variance.
The heterogeneity between trials was examined using the I statistic, which indicates the proportion of the total variation in the estimates that is due to between-studies variation.
Additionally, the I statistic was used to measure inconsistency across studies and represents the proportion of variability in the estimates that is due to between-study variation (40).
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CEO of Professional Science Editing for Scientists @ prosciediting.com