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We also explored the potential use of attribute charts for which the strong, and possibly unrealistic, assumption of the ASRM and QIDS scores following a binomial distribution is required.
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To estimate the number of replicate reactions required to achieve a particular number of successful amplifications (at a given probability), the binomial distribution is appropriate.
The sample is drawn without replacement, so, strictly speaking, the binomial distribution is not applicable.
The binomial distribution is now a conditional probability, given the value of r.
A standardized binomial distribution is a binomial distribution scaled and translated so that it has zero mean and unit variance.
Thus, the binomial distribution is a generalization of the Bernoulli distribution.
Binomial distribution is a fundamental statistical assumption about sampling process.
Therefore, the binomial distribution is a conservative model for the sampling process.
The negative binomial distribution is used commonly throughout biology as a model for overdispersed count data, with attention focused on the negative binomial dispersion parameter, k.
An extension of the binomial distribution is the beta-binomial distribution.
A two-sample, one-sided prospective power analysis (exact binomial distribution) was performed to calculate the sample size required per group in a pair-wise comparison to document a statistically significant increase in 60-day survival outcome from 5% (single modality) to 60% (combination modality).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com