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A stratified random sampling technique was used to minimize sample variance and to increase precision.
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To minimize sampling variance of the estimates of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\sigma}_{a_v} $$\end{document} σ a v, phenotypes on a large number of relatives are required.
To minimize sample variation, we used high quality RNA.
Values of the variance components used in the simulation were assumed known to minimize sampling variation.
To minimize sampling variability, the following steps were taken.
The Taguchi method and the Pareto Analysis of variance were used to elaborate the effects of Brij 78/SiO2 ratio and aging time in (i) minimizing samples mean crystal length (MCL) and (ii) maximizing BET surface areas.
To minimize biological variance, each sample was harvested in three independent biological replicates of equal weight and subsequently pooled for sequencing and volatile analysis.
The sample variance of the sample is defined as (1.2).
Is this just the sample variance?
For each value k, we repeat the experiment 20 times to minimize effects due to sampling variance.
To minimize the impact of sampling variance on results, valid comparisons were those that had a minimum of 20× coverage.
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