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This module explains the concept of independent events, where the probability of event A does not have any effect on the probability of event B, and mutually exclusive events, where events A and B cannot occur at the same time. It is based on the module Probability Topics: Independent and Mutually Exclusive Events from the textbook collection Collaborative Statistics by Dr. Barbara Illowsky and Susan Dean; an example has been added illustrating the determination that two events are not independent.

Independent events

Two events are independent if the following are true:

  • P(A|B) = P(A)
  • P(B|A) = P(B)
  • P(A AND B) = P(A) ⋅ P(B)

If events A and B are independent , then the chance of A occurring does not affect the chance of B occurring and vice versa.

Translating the symbols into words, the first two mathematical statements listed above say that the probability for the event with the condition is the same as the probability for the event without the condition. For independent events: the condition does not change the probability for the event.

For example, two roles of a fair die are independent events. The outcome of the first roll does not change the probability for the outcome of the secondroll.

If you select 2 cards consecutively from a complete deck of playing cards, the two selections are not independent ; the result of the first selection changes the remaining deck and affects the probabilities for the second selection. This is referred to as selecting "without replacement"; the first card has not been replaced into the deck before the second card is selected.

However, suppose you were to select 2 cards "with replacement", by returning your first card to the deck and shuffling the deck before selecting the second card. Because the deck of cards is complete for both selections, the first selection does not affect the probability of the second selection. When selecting cards with replacement, the selections are independent .

To show that two events are independent, you must show only one of the conditions listed above. If any one of these conditions is true, then all of them are true.

Mutually exclusive events

Events A and B are mutually exclusive events if they cannot occur at the same time. This means that A and B do not share any outcomes and P(A AND B) = 0 .

  • For example, suppose the sample space S = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10} .
  • Let A = {1, 2, 3, 4, 5}, B = {4, 5, 6, 7, 8} , and C = {7, 9} .
  • A AND B = { 4 , 5 } . P(A AND B) = 2 10 and is not equal to zero. Therefore, A and B are not mutually exclusive.
  • A and C do not have any numbers in common so P(A AND C) = 0 . Therefore, A and C are mutually exclusive.
Independent and mutually exclusive do not mean the same thing.

You must show that any two events are independent or mutually exclusive. You cannot assume either of these conditions.

If it is not known whether A and B are independent or dependent, assume they are dependent until you can show otherwise .

The following examples illustrate these definitions and terms.

Flip two fair coins. (This is an experiment.)

The sample space is {HH, HT, TH, TT} where T = tails and H = heads. The outcomes are HH , HT , TH , and TT . The outcomes HT and TH are different. The HT means that the first coin showed heads and the second coin showed tails. The TH means that the first coin showed tails and the second coin showed heads.

  • Let A = the event of getting at most one tail . (At most one tail means 0 or 1 tail.) Then A can be written as {HH, HT, TH} . The outcome HH shows 0 tails. HT and TH each show 1 tail.
  • Let B = the event of getting all tails. B can be written as {TT} . B is the complement of A . So, B = A' . Also, P(A) + P(B) = P(A) + P(A') = 1 .
  • The probabilities for A and for B are P(A) = 3 4 and P(B) = 1 4 .
  • Let C = the event of getting all heads. C = {HH} . Since B = {TT} , P(B AND C) = 0 . B and C are mutually exclusive. ( B and C have no members in common because you cannot have all tails and all heads at the same time.)
  • Let D = event of getting more than one tail. D = {TT} . P(D) = 1 4 .
  • Let E = event of getting a head on the first roll. (This implies you can get either a head or tail on the second roll.) E = { HT , HH } . P(E) = 2 4 .
  • Find the probability of getting at least one (1 or 2) tail in two flips. Let F = event of getting at least one tail in two flips. F = { HT , TH , TT } . P(F) = 3 4
Practice Key Terms 2

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Source:  OpenStax, Collaborative statistics: custom version modified by r. bloom. OpenStax CNX. Nov 15, 2010 Download for free at http://legacy.cnx.org/content/col10617/1.4
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