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To arrive at a statistical significance level (p-value), one can use a permutation test (described in Methods).
More appropriately, one might use a permutation test which is not based on the assumption of randomisation.
This asymmetrical nature of the analysis is the prime reason why it is necessary to use a permutation test to evaluate the significance of the results.
Therefore, we use a permutation test to assign statistical significance (q-values) to the multiple-species association scores, S t, as we describe below.
Our approach was to use a permutation test to empirically derive a statistical threshold and consequently reduce the number of false-positives due to multiple hypotheses being tested [ 23].
Clover can use a permutation test to see how often a given matrix is found in a group of sequences when compared to how often it is found in a randomly generated sequence set selected from background sequences that maintain the same number of sequences, the same length, and same G/C content as the input set.
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To test the significance of this plot, we used a permutation test (permuting the designation of an "exercise related gene") and as summary test statistic the average squared log2 ratios (the average Euclidean distance from the log2(1)).
The newly identified genes were further filtered using a permutation test.
Empirical values for chromosome-wise significance thresholds were determined using a permutation test.
The significance of the difference between two obtained ROC areas was calculated using a permutation test [12].
When comparing two groups (comparisons between cultivars), a t test was used instead, in case of unequal variances p value was determined using a permutation test.
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