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.
We use a binomial test to assess the statistical significance of the activity scores (see Methods).
As before, we can apply a binomial test to assess deviations from the expected distribution which would indicate that the abnormal region has ended and initiate boundary retraction.
Second, we calculated the proportion proteins for which ρ y, x1) > ρ y, x2) and used a binomial test to assess whether such proportion is larger than 50%.
To statistically assess the temporal pattern of the genetic lesions, we use the binomial test to assess significance by assuming that the number of in-degree and out-degree of each alteration are randomly distributed.
For genes that overlapped across the two experiments' lists, we used a binomial test to assess if genes up-regulated in the LSB line were also up-regulated with anxiolytic drug treatment in [ 24].
We also used a Binomial test to assess whether the number of individuals keeping a stable preference differed from the number of individuals with an unstable preference and to analyse whether the number of individuals exhibiting unstable preferences differed due to the technique they favoured in their first year.
We developed the following pipeline to predict miRNA modules (Fig. 1): starting from 18 514 experimentally validated target sites, we modified the ChIPModule approach (Ding et al., 2013) to discover groups of miRNAs that co-bind at least S mRNAs; next, we applied the binomial test to assess the statistical significance of every group of miRNAs identified above.
We used a one-tailed binomial test to assess whether the number of sites in our regions of interest was statistically significantly greater than that in the background region, with a null hypothesis that transcription factor binding sites are equally likely to be found in each of the 200-bp regions.
We calculate both the sequential in-degree (number of arrows to a node) and out-degree (number of arrows from the node) of each genetic lesion in ISN and use the binomial test to assess the significance by assuming that the number of in-degree and out-degree is randomly distributed.
