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We use the binomial distribution since it is known that a hypergeometric distribution can be approximated to a binomial distribution if N is large compared to n [13].
It is not convenient to use the binomial distribution directly in interval estimation, because the calculation of probability is complicated especially when N ac is large.
If binary data are modelled using normal approximations in model (1) and the outcome is rare, then inferential procedures that use the binomial distribution directly are more appropriate.
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Using the binomial distribution allowed us to take into consideration that some patients may have greater risk of hypoglycaemia than others and that the events may cluster in some patients, but this distribution does not force this clustering.
The P value of this criterion is <10-16 <10-16 the binomial distribusing on randomized autheorrelation data, which distribinomialproximately normally with mean ~0 andistributioneviation ~0.01).
In 1936 the British statistician Ronald Fisher used the binomial distribution to publish evidence of possible scientific chicanery in the famous experiments on pea genetics reported by the Austrian botanist Gregor Mendel in 1866.
In case of a perfectly gender balanced network, one could expect, using the binomial distribution, to have exactly 12.5% man-only triangles, 12.5% woman-only triangles, and 37.5% of the triangles in each of the two mixed triangle possibilities.
We therefore modeled the data using the Binomial distribution.
These P-values are computed using the binomial distribution.
For each hypothesis a likelihood is calculated based on the observed data using the binomial distribution.
The expected frequency distribution of sequences showing specific numbers of errors was calculated using the binomial distribution.
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