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  2. Continuity correction - Wikipedia

    en.wikipedia.org/wiki/Continuity_correction

    A particular example of this is the binomial test, involving the binomial distribution, as in checking whether a coin is fair. Where extreme accuracy is not necessary, computer calculations for some ranges of parameters may still rely on using continuity corrections to improve accuracy while retaining simplicity.

  3. Binomial distribution - Wikipedia

    en.wikipedia.org/wiki/Binomial_distribution

    In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own Boolean-valued outcome: success (with probability p) or failure (with probability q = 1 − p).

  4. Coverage probability - Wikipedia

    en.wikipedia.org/wiki/Coverage_probability

    [1] In statistical prediction, the coverage probability is the probability that a prediction interval will include an out-of-sample value of the random variable . The coverage probability can be defined as the proportion of instances where the interval surrounds an out-of-sample value as assessed by long-run frequency .

  5. 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.

  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

  7. Minimax estimator - Wikipedia

    en.wikipedia.org/wiki/Minimax_estimator

    Example 1: Unfair coin [2] [3]: Consider the problem of estimating the "success" rate of a binomial variable, (,). This may be viewed as estimating the rate at which an unfair coin falls on "heads" or "tails".

  8. Binomial test - Wikipedia

    en.wikipedia.org/wiki/Binomial_test

    The binomial test is useful to test hypotheses about the probability of success: : = where is a user-defined value between 0 and 1.. If in a sample of size there are successes, while we expect , the formula of the binomial distribution gives the probability of finding this value:

  9. Binomial process - Wikipedia

    en.wikipedia.org/wiki/Binomial_process

    The name of a binomial process is derived from the fact that for all measurable sets the random variable follows a binomial distribution with parameters () and : ξ ( A ) ∼ Bin ⁡ ( n , P ( A ) ) . {\displaystyle \xi (A)\sim \operatorname {Bin} (n,P(A)).}