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  2. Checking whether a coin is fair - Wikipedia

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

    This article describes experimental procedures for determining whether a coin is fair or unfair. There are many statistical methods for analyzing such an experimental procedure. This article illustrates two of them. Both methods prescribe an experiment (or trial) in which the coin is tossed many times and the result of each toss is recorded.

  3. Coin flipping - Wikipedia

    en.wikipedia.org/wiki/Coin_flipping

    The three-way flip is 75% likely to work each time it is tried (if all coins are heads or all are tails, each of which occur 1/8 of the time due to the chances being 0.5 by 0.5 by 0.5, the flip is repeated until the results differ), and does not require that "heads" or "tails" be called.

  4. Bernoulli trial - Wikipedia

    en.wikipedia.org/wiki/Bernoulli_trial

    A representation of the possible outcomes of flipping a fair coin four times in terms of the number of heads. As can be seen, the probability of getting exactly two heads in four flips is 6/16 = 3/8, which matches the calculations. For this experiment, let a heads be defined as a success and a tails as a failure.

  5. Gambler's fallacy - Wikipedia

    en.wikipedia.org/wiki/Gambler's_fallacy

    When flipping a fair coin 21 times, the outcome is equally likely to be 21 heads as 20 heads and then 1 tail. These two outcomes are equally as likely as any of the other combinations that can be obtained from 21 flips of a coin. All of the 21-flip combinations will have probabilities equal to 0.5 21, or 1 in 2,097,152. Assuming that a change ...

  6. Law of large numbers - Wikipedia

    en.wikipedia.org/wiki/Law_of_large_numbers

    For example, a fair coin toss is a Bernoulli trial. When a fair coin is flipped once, the theoretical probability that the outcome will be heads is equal to 1 ⁄ 2. Therefore, according to the law of large numbers, the proportion of heads in a "large" number of coin flips "should be" roughly 1 ⁄ 2.

  7. Outcome (probability) - Wikipedia

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

    For the experiment where we flip a coin twice, the four possible outcomes that make up our sample space are (H, T), (T, H), (T, T) and (H, H), where "H" represents a "heads", and "T" represents a "tails". Outcomes should not be confused with events, which are sets (or informally, "groups") of outcomes. For comparison, we could define an event ...

  8. How Many Times Should You Flip A Steak? - AOL

    www.aol.com/many-times-flip-steak-175900742.html

    How Many Times Should You Flip A Steak? Gabby Romero. July 19, 2024 at 10:59 AM. Cooking a steak can be one of the most daunting tasks in the kitchen.

  9. Fair coin - Wikipedia

    en.wikipedia.org/wiki/Fair_coin

    In theoretical studies, the assumption that a coin is fair is often made by referring to an ideal coin. John Edmund Kerrich performed experiments in coin flipping and found that a coin made from a wooden disk about the size of a crown and coated on one side with lead landed heads (wooden side up) 679 times out of 1000. [1]