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In summary, our data show that both experimental and statistical variability are important sources of different outcomes between laboratories.
Extensive data analysis showed that comparison of genomics data from different sources is complex because both experimental and statistical variability are important confounding factors.
Quantifying narrow-sense heritability, the proportion of phenotypic variance attributable to additive genetic effects (Lynch and Walsh 1998), for nonmodel organisms presents both experimental and statistical challenges.
The apparent conflict, however, can be explained by differences in both experimental and statistical approaches used here and elsewhere (Scott et al, 1998; Burrill et al, 2000; Baeyens et al, 2002).
However, estimating the heritability of these traits presents both experimental and statistical challenges, which typically arise from logistical difficulties associated with rearing large numbers of families independently in the field, a lack of known pedigree, the need to account for group or batch effects, etc.
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Although the role of introns in stability of mRNA has been reported both in experimental and statistical studies, here we present the first evidence that the number of exon junctions per unit ORF length is the most important known structural factor that increases mRNA stability.
We need to build causal models through both experimental designs and statistical causal modeling so that we can better understand how social media use affects students and more importantly, how we can use social media with our students in order to maximize educational outcomes.
To take account of both experimental and inter-individual variability, a statistical strategy was adopted.
A model should include mechanistic, experimental, and statistical aspects.
Furthermore, MAM is subject to significant experimental and statistical uncertainties.
Experimental and statistical results show high accuracy in the detection of incipient broken bars.
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