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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%).
In probability theory, an outcome is a possible result of an experiment or trial. [1] Each possible outcome of a particular experiment is unique, and different outcomes are mutually exclusive (only one outcome will occur on each trial of the experiment). All of the possible outcomes of an experiment form the elements of a sample space. [2]
Probability theory or probability calculus is the branch of mathematics concerned with probability.Although there are several different probability interpretations, probability theory treats the concept in a rigorous mathematical manner by expressing it through a set of axioms.
The outcomes must be collectively exhaustive, i.e. on every experiment (or random trial) there will always take place some outcome for {,, …,}. [ 6 ] The sample space ( Ω {\displaystyle \Omega } ) must have the right granularity depending on what the experimenter is interested in. Irrelevant information must be removed from the sample space ...
These definitions are equivalent, since dividing both terms in the ratio by the number of outcomes yields the probabilities: : = (/): (/). Conversely, the odds against is the opposite ratio. For example, the odds against a random day of the week being during a weekend are 5:2.
The classical definition of probability assigns equal probabilities to events based on physical symmetry which is natural for coins, cards and dice. Some mathematicians object that the definition is circular. [11] The probability for a "fair" coin is... A "fair" coin is defined by a probability of... The definition is very limited.
In mathematics, a probability measure is a real-valued function defined on a set of events in a σ-algebra that satisfies measure properties such as countable additivity. [1] The difference between a probability measure and the more general notion of measure (which includes concepts like area or volume ) is that a probability measure must ...
A is assumed to be the set of all possible outcomes of an experiment or random trial that has a restricted or reduced sample space. The conditional probability can be found by the quotient of the probability of the joint intersection of events A and B , that is, P ( A ∩ B ) {\displaystyle P(A\cap B)} , the probability at which A and B occur ...