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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.
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.
For example, rolling an honest die produces one of six possible results. One collection of possible results corresponds to getting an odd number. Thus, the subset {1,3,5} is an element of the power set of the sample space of dice rolls. These collections are called events. In this case, {1,3,5} is the event that the die falls on some odd number.
A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the two outcomes ("heads" and "tails") are both equally probable; the probability of "heads" equals the probability of "tails"; and since no other outcomes are possible, the probability of either "heads" or "tails" is 1/2 (which could also be written as 0.5 or 50%).
This is not the case for arbitrary coin systems or even some real world systems, though. For instance, if we consider the old (now withdrawn) Indian coin denominations of 5, 10, 20 and 25 paise, then to make 40 paise, the greedy algorithm would choose three coins (25, 10, 5) whereas the optimal solution is two coins (20, 20).
As visitors' coins splash into Rome's majestic Trevi Fountain carrying wishes for love, good health or a return to the Eternal City, they provide practical help to people the tourists will never meet.
Lack of coin-counting machines: Many national banks have removed or stopped offering automatic coin-counting machines to customers. Local banks or credit unions are more likely to have them, but ...
A Bernoulli process is a finite or infinite sequence of independent random variables X 1, X 2, X 3, ..., such that . for each i, the value of X i is either 0 or 1;; for all values of , the probability p that X i = 1 is the same.