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The Mean Ranking is basically the sum of ranks obtained by a method in each dataset divided by the total number of datasets, as below: mathit{MR} = frac{sum_{j=1}^{mathit{nd}}mathit{ri}}{mathit{nd}} where nd is the number of datasets and ri is the rank of the method for dataset i.
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The overall mean ranking was 6.5/10 (Table 3).
Method A was deemed better than B only if A's mean ranking was greater than B's in 90% (3600) of the bootstraps.
The question type with the highest mean rank is considered to have higher confidence ratings for a user type.
However the use of the mean rank is itself problematic as the pairwise similarity of two methods can be altered (and even inverted) by adding additional methods to an evaluation.
In a protein of size 300 amino acids, the HLA ligand will thus on average be ranked 4. The mean rank is 4.4%.
It shows that when DCM level increases the mean rank is increasing.
The same conclusion can be reached when comparing the medical with neurosurgical ICU, where the difference between mean ranks is 4.9.
The mean rank is 1000.[ 19] The SEIFA values in this study are based upon the 2001 Australian Census of Population and Housing.
Treatments i and j are significantly different at the 5% significance level if the difference between their mean ranks is greater than the least significant difference (i.e. if the following inequality is true): Where t is the value from the t distribution for a 5% significance level and N - k degrees of freedom.
The surface under the cumulative ranking curve (SUCRA), which is a simple transformation of the mean rank, was used to provide a hierarchy of the treatments.
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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