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First, determine a separating event a feature of the data more likely to belong to one distribution than the other then use that event to generate a preliminary partition of the data.
As the Rw values were logarithmized (log(x+1)) in order that the distribution of data more likely approximates a normal distribution [34], the criterion cutoff lies at log(1.5+1) = .92.92
We found that performing preliminary clustering analyses improve the accuracy of our results by making correct matches between optical mapping data and sequence data more likely.
Calculations may be based on unrealistically large effect sizes that make data more likely to show a statistically significant result [ 11], or conclusions may be based on statistically insignificant differences [ 10] or inappropriate generalizations from findings [ 12].
Because our dataset comprised protein-coding genes and rRNA, we performed partitioned analyses implementing separate nucleotide substitution models for subsets of the data more likely to have experienced similar evolutionary processes (mitochondrial genes, EF and rRNA stems and loops).
These data more likely reflect patients' propensity to consult, the willingness of family doctors to refer, access to hospital beds and the availability of alternative facilities provided by the private sector.[ 39] Coronary heart disease is increasingly common with advancing age and has a significant impact on daily life.
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Studies specifically aimed at assessing tree survival are unusual and so data initially designed for other purposes often need to be used; such data are more likely to contain errors than data collected for this specific purpose.
Compared with small studies with null data, large studies with null data are more likely to be published.
For instance, compared with the analysis on weekly data, the analysis on daily data is more likely to capture more precise association between weather variables and the disease occurrence.
The more information we have about the data, the more likely we would be able to succeed in assessing the true class for a single data item [27].
There is potential for publication prejudice where positive data is more likely to appear in print than negative data.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com