The phrase "affected by variance" is correct and usable in written English.
It can be used in contexts where you want to describe something that is influenced or changed by variability or differences in data, conditions, or circumstances.
Example: "The results of the experiment were significantly affected by variance in temperature."
Alternatives: "influenced by variability" or "impacted by fluctuations."
A limitation to the ICC is that it is highly affected by variance, because if the total variance is small, then the unique variance of each rated target must necessarily be even smaller.
We show that group benefits are strongly affected by variance in foraging success within a reproductive season, constraints on reproduction determined by the reproduction threshold, and investment in offspring production after passing this threshold.
Beside population variability the standard errors in change estimates are generally affected by two variance components: (1) the variance introduced by biomass growth, which is influenced by stand structure and site quality, and (2) the variance due to disturbances, i.e., degradation and deforestation.
In addition, the CO and hydrogen surface coverage fractions are also affected by the variance of the above mentioned parameters.
Importantly, values of R inferred from phylogenetic trees may not relate linearly to the number of infected people, and may also be strongly affected by the variance on the number of potential infectious contacts events.
It may also be necessary to validate more than three reference genes in order to identify at least three that are not affected by biological variance as shown in this report where validation of as many as six reference genes during adipocyte differentiation failed to identify three reference genes that displayed stable expression suitable for inclusion in geometric mean analyses.
FASTA and QTDT models were practically not affected by polygenic variance and relationships.
Consequently, there is little understanding of how the main statistical classifiers, α and β (Table 1), are affected by Gaussian variance through the mechanism of coverage.
Therefore, selection on these three amino acids may be very weak, and the observations are likely to be heavily affected by sampling variance, as suggested by the large confidence intervals associated with these estimates (fig. 1).
The empirical null distribution in GWAS is affected by global variance inflation due to population stratification and cryptic relatedness [ 15] and deflation due to the over-correction caused by the application of standard genomic control methods to polygenic traits [ 16].
