  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. a single outcome may be an element of many different events, and different events in an experiment are usually not equally likely, since they may include very different groups of outcomes. an event defines a complementary event, namely the complementary set (the event not occurring), and together these define a bernoulli trial: did the event occur or not? typically, when the sample space is finite, any subset of the sample space is an event (i.e. all elements of the power set of the sample space are defined as events). however, this approach does not work well in cases where the sample space is uncountably infinite, most notably when the outcome is a real number. so, when defining a probability space it is possible, and often necessary, to exclude certain subsets of the sample space from being events (see events in probability spaces, below). ==a simple example== if we assemble a deck of 52 playing cards with no jokers, and draw a single card from the deck, then the sample space is a 52-element set, as each card is a possible outcome. an event, however, is any subset of the sample space, including any singleton set (an elementary event), the empty set (an impossible event, with probability zero) and the sample space itself (a certain event, with probability one). other events are proper subsets of the sample space that contain multiple elements. so, for example, potential events include:  * "red and black at the same time without being a joker&
Probability_theory