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In probability theory, an event is a set of outcomes of an experiment (a subset of the sample space) to which a probability is assigned. [1] A single outcome may be an element of many different events, [2] and different events in an experiment are usually not equally likely, since they may include very different groups of outcomes. [3]
The Apollo 11 moon landing is generally considered to have been the most watched event in American television history with an estimated viewership of between 125 and 150 million viewers. [1] However, the Apollo 11 moon landing is considered a news event and not a telecast, as the major networks each produced their own coverage of the event.
Although numbers are unreliable, it is generally accepted that the most widely watched events worldwide are international sporting events such as the Summer Olympic Games, FIFA World Cup, [3] and the Cricket World Cup [4] with annual events such as the Super Bowl in the U.S., the Indian Premier League in India [5] and the UEFA Champions League ...
Oct. 29—Two weeks after the 2024 Albuquerque International Balloon Fiesta, official numbers have been finalized. The following numbers are the official statistics for the 2024 Balloon Fiesta ...
The red oval is the event that a number is odd, and the blue oval is the event that a number is prime. A sample space can be represented visually by a rectangle, with the outcomes of the sample space denoted by points within the rectangle. The events may be represented by ovals, where the points enclosed within the oval make up the event. [12]
Since the same outcome may be a member of many events, it is possible for many events to have happened given a single outcome. For example, when the trial consists of throwing two dice, the set of all outcomes with a sum of 7 pips may constitute an event, whereas outcomes with an odd number of pips may constitute another event. If the outcome ...
The probability of an event is a number between 0 and 1; the larger the probability, the more likely an event is to occur. [note 1] [1] [2] 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 ...
The use of pseudo-random numbers as opposed to true random numbers is a benefit should a simulation need a rerun with exactly the same behavior. One of the problems with the random number distributions used in discrete-event simulation is that the steady-state distributions of event times may not be known in advance.