Exact(10)
To test for statistical significance, we used 9999 simulations to compare the K statistic generated from the real data to a distribution of randomized values.
PERMANOVA relies on comparing the observed value of a test statistic (F-ratio) against a recalculated test statistic generated from random permutation of the data.
with background expectations for that statistic generated from genome-wide data, which should reflect neutrality.
The F statistic generated from the means of each cryopreservative in the pellet state suggests that these differences in ratios are real (Table 2).
However, the F statistic generated from the mean number of cells recovered from each treatment suggests that these differences are not likely to be real (Table 2).
The F statistic generated from the mean number of cells recovered from each treatment suggests that the PBS treatment truly did not support recovery as well as did the FBS or RPMI-based preservatives.
Similar(50)
Statistical significance for MDR models was obtained using the R2 statistic generated by comparing the observed prediction error for each MDR model to the null distribution obtained from 10,000 permutations.
Spatial normalization for the purpose of random effects group analyses was then carried out on the t-statistic maps generated from regression analyses performed on native space EPI time series data.
Significance levels were calculated by comparing the R statistic against the distribution generated from 10,000 permutations of the randomized dataset.
Class accuracy, class precision, and a kappa statistic can all be generated from the OOB confusion matrix to evaluate model performance.
Significance was evaluated by comparing the observed test statistic to a distribution generated from 1000 permutations of the original data such that p values represent the proportion of the distribution that is less than or equal to the observed value.
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