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The two events are said to be independent.

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Q: When the probability of one event occurred is not affected by a second event having already occurred the two events are said to be?

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They are "events that have the same probability". Nothing more, nothing less.

Independent events.

Multiply the possible outcomes of the events in the disjoint events

Yes. Independent events can exist in reality. Dependent events means that one event has had an effect on the other. For instance, if we look at the probability of someone going to the shops, and the probability of them buying an apple, the latter is clearly dependent on the former. Independent events are simply events that don't have this connection. The probability of one does not influence or predict the probability of the other. For instance, if I studied the probability of you going to see a film on a particular day, and the probability of someone in China getting a hole in one in golf, these are very clearly independent events.

No, the combined probability is the product of the probability of their separate occurrances.

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It can be called a "conditional probability", but the word "conditional" is irrelevant if the two events are independent.

If it is a fair die and rolled fairly, the two events are independent so that the probability is 1/6.

A dependent probability.

Historical events which have occurred have a probability of 1. They are a certainty. This refers to the event itself, not some historian's or politician's interpretation of what happened. However, the probability that they will occur again depends on the event. Exact recurrence is impossible (probability = 0).

If events A and B are statistically indepnedent, then the conditional probability of A, given that B has occurred is the same as the unconditional probability of A. In symbolic terms, Prob(A|B) = Prob(A).

They are "events that have the same probability". Nothing more, nothing less.

Independent events with a probability of zero

Addition Theorem The addition rule is a result used to determine the probability that event A or event B occurs or both occur. ; The result is often written as follows, using set notation: : ; where: : P(A) = probability that event A occurs : P(B) = probability that event B occurs : = probability that event A or event B occurs : = probability that event A and event B both occur ; For mutually exclusive events, that is events which cannot occur together: : = 0 ; The addition rule therefore reduces to : = P(A) + P(B) ; For independent events, that is events which have no influence on each other: : ; The addition rule therefore reduces to : ; Example ; Suppose we wish to find the probability of drawing either a king or a spade in a single draw from a pack of 52 playing cards. ; We define the events A = 'draw a king' and B = 'draw a spade' ; Since there are 4 kings in the pack and 13 spades, but 1 card is both a king and a spade, we have: : = 4/52 + 13/52 - 1/52 = 16/52 ; So, the probability of drawing either a king or a spade is 16/52 (= 4/13).MultiplicationTheorem The multiplication rule is a result used to determine the probability that two events, A and B, both occur. The multiplication rule follows from the definition of conditional probability. ; The result is often written as follows, using set notation: : ; where: : P(A) = probability that event A occurs : P(B) = probability that event B occurs : = probability that event A and event B occur : P(A | B) = the conditional probability that event A occurs given that event B has occurred already : P(B | A) = the conditional probability that event B occurs given that event A has occurred already ; For independent events, that is events which have no influence on one another, the rule simplifies to: : ; That is, the probability of the joint events A and B is equal to the product of the individual probabilities for the two events.

The Supreme Court decided a case that directly affected the outcome of the election.

That probability is the product of the probabilities of the two individual events; for example, if event A has a probability of 50% and event B has a probability of 10%, the probability that both events will happen is 50% x 10% = 5%.

Given two events, A and B, the conditional probability rule states that P(A and B) = P(A given that B has occurred)*P(B) If A and B are independent, then the occurrence (or not) of B makes no difference to the probability of A happening. So that P(A given that B has occurred) = P(A) and therefore, you get P(A and B) = P(A)*P(B)

It depends on the events. The answer is 0.5*(Total number of events - number of events with probability = 0.5) That is, discount all events such that their probability (and that of their complement) is exactly a half. Then half the remaining events will have probabilities that are greater than their complement's.

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