Lumen๐Ÿช™ 0

Mutually exclusive outcomes

๐Ÿ“– Learn๐ŸŽฌ Guided Practiceโœ๏ธ Practice

Probability

Mutually exclusive outcomes

If you roll a 1 on a dice, you cannot also have rolled a 3. Outcomes that cannot happen together are mutually exclusive โ€” and if they cover everything, their probabilities add to 1.

Mutually exclusiveroll a1roll a3no overlap โ€” one throwcannot be bothNOT mutually exclusiverainwinda day can easily beboth wet and windyBANANA: P(B) = 1/6, P(A) = 3/6, P(N) = 2/61/6 + 3/6 + 2/6 = 6/6 = 1 โ€” they cover everything
Cover every outcome and the probabilities add to 1.

Outcomes that cannot happen together

Two outcomes are MUTUALLY EXCLUSIVE when they cannot both happen at the same time. Rolling a 1 and rolling a 3 are mutually exclusive โ€” one roll cannot be both. Rain and strong wind are NOT mutually exclusive. A day can easily be both wet and windy.

Equally likely outcomes

When every outcome is EQUALLY LIKELY, you do not need an experiment. You can calculate. On a fair dice, 1, 2, 3, 4, 5 and 6 are all equally likely. But for a football match, winning 1โ€“0 and winning 6โ€“0 are NOT equally likely โ€” so you cannot just count outcomes there.

Theoretical probability

When outcomes are equally likely, probability = number of ways it CAN happen รท total number of outcomes Ten cards show 3, 5, 5, 8, 5, 9, 8, 5, 3, 8. One is taken without looking. P(5) = 4/10 = 2/5 = 40% P(8) = 3/10 = 30% P(1) = 0/10 = 0 โ€” impossible

Adding to 1

If a set of mutually exclusive outcomes covers everything that could happen, their probabilities add to 1. BANANA has 6 letters: 1 B, 3 A, 2 N. P(B) = 1/6, P(A) = 3/6, P(N) = 2/6 1/6 + 3/6 + 2/6 = 6/6 = 1 โœ“ This gives a shortcut: P(not A) = 1 โˆ’ P(A).

Marcus's mistake

Marcus says: 'My team can win, draw or lose. Winning is one of three outcomes, so P(win) = 1/3.' That is wrong. Dividing by the number of outcomes only works when the outcomes are EQUALLY LIKELY โ€” and win, draw and lose are not. Three outcomes does not mean one-third each.

Worked examples

Ten cards: 3, 5, 5, 8, 5, 9, 8, 5, 3, 8 โ€” P(5)?

  1. Count the 5s: there are 4.
  2. There are 10 cards altogether.
  3. 4/10 simplifies to 2/5.

Answer: 2/5, or 40%

P(8) from the same ten cards

  1. Count the 8s: there are 3.
  2. 3 out of 10.

Answer: 3/10, or 30%

P(1) from the same ten cards

  1. No card shows a 1.
  2. 0 out of 10 is impossible.

Answer: 0

P(an even number) on a fair dice

  1. The even faces are 2, 4 and 6 โ€” that is 3 outcomes.
  2. There are 6 faces altogether.
  3. 3/6 simplifies to 1/2.

Answer: 1/2

BANANA โ€” do the probabilities add to 1?

  1. P(B) = 1/6, P(A) = 3/6, P(N) = 2/6.
  2. 1/6 + 3/6 + 2/6 = 6/6.

Answer: Yes โ€” they add to 1, because they cover every card

Six boys and four girls โ€” P(not a girl's name)?

  1. P(girl) = 4/10 = 2/5.
  2. Use P(not A) = 1 โˆ’ P(A).
  3. 1 โˆ’ 2/5 = 3/5.

Answer: 3/5

Where you see this in real life

  • A raffle where each ticket is equally likely to win. Your chance is just your tickets over the total sold.
  • Quality checks on a production line assume each item is equally likely to be picked, so the sample represents the batch.

Key words

Mutually exclusive
two outcomes that cannot both happen
Equally likely
outcomes with the same chance of happening
Theoretical probability
a probability worked out by counting, not by experiment
Outcome
one possible result
Fair
having every outcome equally likely; also called unbiased