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  2. Notation in probability and statistics - Wikipedia

    en.wikipedia.org/wiki/Notation_in_probability...

    The α-level upper critical value of a probability distribution is the value exceeded with probability , that is, the value such that () =, where is the cumulative distribution function. There are standard notations for the upper critical values of some commonly used distributions in statistics:

  3. Glossary of probability and statistics - Wikipedia

    en.wikipedia.org/wiki/Glossary_of_probability...

    Also confidence coefficient. A number indicating the probability that the confidence interval (range) captures the true population mean. For example, a confidence interval with a 95% confidence level has a 95% chance of capturing the population mean. Technically, this means that, if the experiment were repeated many times, 95% of the CIs computed at this level would contain the true population ...

  4. Probability - Wikipedia

    en.wikipedia.org/wiki/Probability

    A probability is a way of assigning every event a value between zero and one, with the requirement that the event made up of all possible results (in our example, the event {1,2,3,4,5,6}) is assigned a value of one. To qualify as a probability, the assignment of values must satisfy the requirement that for any collection of mutually exclusive ...

  5. I Ching divination - Wikipedia

    en.wikipedia.org/wiki/I_Ching_divination

    The two-coin method involves tossing one pair of coins twice: on the first toss, two heads give a value of 2, and anything else is 3; on the second toss, each coin is valued separately, to give a sum from 6 to 9, as above. This results in the same distribution of probabilities as for the yarrow-stalk method.

  6. Bernoulli distribution - Wikipedia

    en.wikipedia.org/wiki/Bernoulli_distribution

    It can be used to represent a (possibly biased) coin toss where 1 and 0 would represent "heads" and "tails", respectively, and p would be the probability of the coin landing on heads (or vice versa where 1 would represent tails and p would be the probability of tails). In particular, unfair coins would have /

  7. Complementary event - Wikipedia

    en.wikipedia.org/wiki/Complementary_event

    For example, if a typical coin is tossed and one assumes that it cannot land on its edge, then it can either land showing "heads" or "tails." Because these two outcomes are mutually exclusive (i.e. the coin cannot simultaneously show both heads and tails) and collectively exhaustive (i.e. there are no other possible outcomes not represented ...

  8. Coupling (probability) - Wikipedia

    en.wikipedia.org/wiki/Coupling_(probability)

    Intuitively, if both coins are tossed the same number of times, we should expect the first coin turns up fewer heads than the second one. More specifically, for any fixed k , the probability that the first coin produces at least k heads should be less than the probability that the second coin produces at least k heads.

  9. Bernoulli process - Wikipedia

    en.wikipedia.org/wiki/Bernoulli_process

    This value is the Bernoulli entropy of a Bernoulli process. Here, H stands for entropy; not to be confused with the same symbol H standing for heads. John von Neumann posed a question about the Bernoulli process regarding the possibility of a given process being isomorphic to another, in the sense of the isomorphism of dynamical systems.