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In probability theory, the complement of any event is the event , i. e. the event that does not occur. The event and its complement are mutually exclusive and exhaustive. Generally, there is only one event such that and are both mutually exclusive and exhaustive; that event is the complement of . The complement of an event is usually denoted as , or . Given an event, the event and its complementary event define a Bernoulli trial: did the event occur or not?

For example, if a player reveals the Tech card of a Daily Kit Box Daily Kit Box † which has the hidden rule of only granting Advanced and Mid-Tech, then the card can either be Advanced Tech or Mid-Tech. Because these two outcomes are mutually exclusive (i. e. the card cannot simultaneously show both Advanced and Mid-Tech) and collectively exhaustive (i. e. there are no other possible outcomes not represented between these two), they are therefore each other's complements. This means that is logically equivalent to , and is equivalent to .

Complement rule[]

In a random experiment, the probabilities of all possible events (the sample space) must total to 1—that is, some outcome must occur on every trial. For two events to be complements, they must be collectively exhaustive, together filling the entire sample space. Therefore, the probability of an event's complement must be 1 minus the probability of the event. That is, for an event ,

Equivalently, the probabilities of an event and its complement must always total to 1. This does not, however, mean that any two events whose probabilities total to 1 are each other's complements; complementary events must also fulfill the condition of mutual exclusivity.