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We use a binomial test to assess the statistical significance of the activity scores (see Methods).
As a consequence, we decided to use a binomial test for proportions on discretized score distributions.
We use a binomial test whether or not an edge should be included in the resulting network.
With the two possible orders of a given pair, and the occurrence count of each, we use a binomial test to assign a confidence to each pair order, thus providing the probability of a particular order occurring a given number of times by chance.
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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].
A second value of using a binomial test is that the output is quite clearly reliant upon the value of p which needs to be justified, and this process can make apparent any unreliable assumptions.
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.
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