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We used a binomial test to assess significant trends in fruit length, fruit diameter, fresh fruit mass, number of seeds per fruit, and seed mass associated with megafaunal dispersal.
In all the trail-following experiments we used a binomial test to discriminate between two possible outcomes of the experiments; H0 – that males followed male and female trails for equal distances (or followed male distances longer than female distances); H1 – that males followed female mucous trails for longer distances than they followed other male trails.
For the binary variables, we used a binomial test.
For the group comparison of men versus women we used a binomial test procedure.
To assess whether the birds' success rates differed from chance, we used a Binomial test.
Therefore, we used a binomial test (see Materials and Methods) to overcome the confounding phylogenetic effect.
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In order to do that, the number of times they appear in the training data containing positive and negative messages are counted, and then are compared with the overall proportion of positive over negative messages using a binomial test.
Using a binomial test I show strong evidence that there is a preference for solar orientations.
An adaptation of the McNemar test, using a binomial test, as suggested by Siegel and Castellan [15], was used to compare success rates.
First, a supervised analysis was done by testing the enrichment of the different clusters in the targets of several well established RBP, such as Pub1p, Nlp3p, Puf3p, or Puf4p [22], [22], using a binomial test.
Significance of the classification outcome was computed using a binomial test, which compares the performance of the trained classifier with that of a baseline classifier that assigns all trials to the most prevalent class [39].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com