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Researchers reported considerable evidence that many statistical and methodological errors were common in published papers and that authors often failed to discuss the limitations of their findings.
Many statistical and geostatistical methods assume such a data model.
Therefore many statistical and numerical modeling techniques have been suggested for the ϕ′ value.
Nonrandom patterns in codon assignments are supported by many statistical and biochemical studies in the last two decades.
However, many statistical and modeling concepts are not well-tested in application settings and their applicability is often seriously delimited.
In many statistical and mechanic models, an END assumption among the random variables in the models is more reasonable than an independent or NA assumption.
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Among these three problems, model deviations and limited sample sizes are very common in many statistical methods, and are not unique to the statistics of the ALT.
During the past many years, order statistics which is used to deal with properties and applications of ordered random variables and of functions of these variables appears in many statistical applications and are widely used in statistical modeling and inference based on some suitable functions of the order statistics.
The present study includes many statistical tests and some of the statistically significant findings in univariate analysis may be due to mass significance rather than causality.
This chapter provides an overview of how the general linear model, the workhorse of functional imaging analysis, provides a single framework for many statistical tests and models, giving great flexibility for experimental design and analysis.
In addition, although our general hypothesis of associations between POPs and incident diabetes were made a priori, we did perform many statistical tests, and caution should be exercised in interpreting the findings.
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