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  2. Coin flipping - Wikipedia

    en.wikipedia.org/wiki/Coin_flipping

    To choose two out of three, three coins are flipped, and if two coins come up the same and one different, the different one loses (is out), leaving two players. To choose one out of three, the previous is either reversed (the odd coin out is the winner ) or a regular two-way coin flip between the two remaining players can decide.

  3. Fair coin - Wikipedia

    en.wikipedia.org/wiki/Fair_coin

    A fair coin, when tossed, should have an equal chance of landing either side up. In probability theory and statistics, a sequence of independent Bernoulli trials with probability 1/2 of success on each trial is metaphorically called a fair coin. One for which the probability is not 1/2 is called a biased or unfair coin.

  4. Checking whether a coin is fair - Wikipedia

    en.wikipedia.org/wiki/Checking_whether_a_coin_is...

    (Note: r is the probability of obtaining heads when tossing the same coin once.) Plot of the probability density f(r | H = 7, T = 3) = 1320 r 7 (1 − r) 3 with r ranging from 0 to 1. The probability for an unbiased coin (defined for this purpose as one whose probability of coming down heads is somewhere between 45% and 55%)

  5. Mutual exclusivity - Wikipedia

    en.wikipedia.org/wiki/Mutual_exclusivity

    In the case of flipping a coin, flipping a head and flipping a tail are also mutually exclusive events. Both outcomes cannot occur for a single trial (i.e., when a coin is flipped only once). The probability of flipping a head and the probability of flipping a tail can be added to yield a probability of 1: 1/2 + 1/2 =1. [5]

  6. Gambler's fallacy - Wikipedia

    en.wikipedia.org/wiki/Gambler's_fallacy

    The probability of getting two heads in two tosses is ⁠ 1 / 4 ⁠ (one in four) and the probability of getting three heads in three tosses is ⁠ 1 / 8 ⁠ (one in eight). In general, if A i is the event where toss i of a fair coin comes up heads, then:

  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. Tree diagram (probability theory) - Wikipedia

    en.wikipedia.org/wiki/Tree_diagram_(probability...

    A tree diagram may represent a series of independent events (such as a set of coin flips) or conditional probabilities (such as drawing cards from a deck, without replacing the cards). [1] Each node on the diagram represents an event and is associated with the probability of that event.

  9. Collectively exhaustive events - Wikipedia

    en.wikipedia.org/wiki/Collectively_exhaustive_events

    "Hence, for any two cardinals M and N, the three relationships M < N, M = N and M > N are 'mutually exclusive', i.e. not more than one of them can hold. ¶ It does not appear till an advanced stage of the theory . . . whether they are 'exhaustive' , i.e. whether at least one of the three must hold". (italics added for emphasis, Kleene 1952:11 ...