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  2. Binomial distribution - Wikipedia

    en.wikipedia.org/wiki/Binomial_distribution

    Therefore, the Poisson distribution with parameter λ = np can be used as an approximation to B(n, p) of the binomial distribution if n is sufficiently large and p is sufficiently small. According to rules of thumb, this approximation is good if n ≥ 20 and p ≤ 0.05 [ 36 ] such that np ≤ 1 , or if n > 50 and p < 0.1 such that np < 5 , [ 37 ...

  3. Negative binomial distribution - Wikipedia

    en.wikipedia.org/wiki/Negative_binomial_distribution

    Different texts (and even different parts of this article) adopt slightly different definitions for the negative binomial distribution. They can be distinguished by whether the support starts at k = 0 or at k = r, whether p denotes the probability of a success or of a failure, and whether r represents success or failure, [1] so identifying the specific parametrization used is crucial in any ...

  4. Binomial proportion confidence interval - Wikipedia

    en.wikipedia.org/wiki/Binomial_proportion...

    The probability density function (PDF) for the Wilson score interval, plus PDF s at interval bounds. Tail areas are equal. Since the interval is derived by solving from the normal approximation to the binomial, the Wilson score interval ( , + ) has the property of being guaranteed to obtain the same result as the equivalent z-test or chi-squared test.

  5. Beta-binomial distribution - Wikipedia

    en.wikipedia.org/wiki/Beta-binomial_distribution

    The beta-binomial distribution is the binomial distribution in which the probability of success at each of n trials is not fixed but randomly drawn from a beta distribution. It is frequently used in Bayesian statistics , empirical Bayes methods and classical statistics to capture overdispersion in binomial type distributed data.

  6. Rule of three (statistics) - Wikipedia

    en.wikipedia.org/wiki/Rule_of_three_(statistics)

    The rule can then be derived [2] either from the Poisson approximation to the binomial distribution, or from the formula (1−p) n for the probability of zero events in the binomial distribution. In the latter case, the edge of the confidence interval is given by Pr( X = 0) = 0.05 and hence (1− p ) n = .05 so n ln (1– p ) = ln .05 ≈ −2.996.

  7. Binomial test - Wikipedia

    en.wikipedia.org/wiki/Binomial_test

    A binomial test is a statistical hypothesis test used to determine whether the proportion of successes in a sample differs from an expected proportion in a binomial distribution. It is useful for situations when there are two possible outcomes (e.g., success/failure, yes/no, heads/tails), i.e., where repeated experiments produce binary data .

  8. McNemar's test - Wikipedia

    en.wikipedia.org/wiki/McNemar's_test

    [citation needed] An exact binomial test can then be used, where b is compared to a binomial distribution with size parameter n = b + c and p = 0.5. Effectively, the exact binomial test evaluates the imbalance in the discordants b and c. To achieve a two-sided P-value, the P-value of the extreme tail should be multiplied by 2. For b ≥ c:

  9. Galton board - Wikipedia

    en.wikipedia.org/wiki/Galton_board

    Galton box A Galton box demonstrated. The Galton board, also known as the Galton box or quincunx or bean machine (or incorrectly Dalton board), is a device invented by Francis Galton [1] to demonstrate the central limit theorem, in particular that with sufficient sample size the binomial distribution approximates a normal distribution.