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The more commonly employed approximate test refers (b−c)2/(b+c) to a chi-square distribution, where b and c are the respective counts of transmitted and untransmitted candidate alleles from heterozygous parents; but, as Spielman et al. [53] noted when proposing this test, "An exact binomial test can be used, if desired".

Thus, a two-tailed binomial test can be applied to identify statistically significant deviations from the expected distribution over a region of the genome, indicating the presence of a chromosomal abnormality.

Hence, such a test can be considered as binomial schema.

Alternatively, a Wald test can be performed, using the reported t statistic for the estimated overdispersion parameter α in the negative binomial model [ 7].

The mapping counts are used to calculate each term's enrichment level in the query list, for which purpose three statistical test methods (binomial, χ, and hypergeometric tests) can be used in EasyGO.

The appropriate test to use is a simple binomial test, which can be approximated by a Chi-squared test with one degree of freedom if expected counts are not too small.

It is referred to as binomial sign test when the test statistic can be approximated with the chi-square distribution, specifically through the use of the chi-square goodness-of-fit.

Unlike the binomial distribution, the beta binomial can be convex as well as concave.

For instance, comparisons between Zero inflated count models with ordinary Poisson, or Zero inflated negative binomial against ordinary negative binomial model can be done using Voung test.

Binomial data can be processed more efficiently on MapReduce.

For smaller regions, the binomial probabilities can be calculated directly.