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  2. Bertrand's box paradox - Wikipedia

    en.wikipedia.org/wiki/Bertrand's_box_paradox

    Bertrand's box paradox: the three equally probable outcomes after the first gold coin draw. The probability of drawing another gold coin from the same box is 0 in (a), and 1 in (b) and (c). Thus, the overall probability of drawing a gold coin in the second draw is ⁠ 0 / 3 ⁠ + ⁠ 1 / 3 ⁠ + ⁠ 1 / 3 ⁠ = ⁠ 2 / 3 ⁠.

  3. 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.

  4. Gambler's fallacy - Wikipedia

    en.wikipedia.org/wiki/Gambler's_fallacy

    While a run of five heads has a probability of ⁠ 1 / 32 ⁠ = 0.03125 (a little over 3%), the misunderstanding lies in not realizing that this is the case only before the first coin is tossed. After the first four tosses in this example, the results are no longer unknown, so their probabilities are at that point equal to 1 (100%).

  5. Bertrand paradox (probability) - Wikipedia

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

    The Bertrand paradox is a problem within the classical interpretation of probability theory. Joseph Bertrand introduced it in his work Calcul des probabilités (1889) [1] as an example to show that the principle of indifference may not produce definite, well-defined results for probabilities if it is applied uncritically when the domain of possibilities is infinite.

  6. 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 ...

  7. 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]

  8. Why do we toss coins into fountains? - AOL

    www.aol.com/why-toss-coins-fountains-160126436.html

    A trip to Rome is not complete without a coin toss backward into the Trevi for the promise of one day returning to the city. ... "Three Coins in the Fountain" cast members throw their loose change ...

  9. 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.