use the enhancement Rule for disjoint events use the General enhancement Rule usage the match Rule

The additional Rule because that Disjoint Events

We"re going to have actually quite a couple of rules in this chapter about probability, yet we"ll start small. The very first situation we desire to look at is once two events have no outcomes in common. We speak to events like this disjoint events.

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Two occasions are disjoint if they have no outcomes in common. (Also commonly known together mutually exclusive events.)

Back in 1881, john Venn developed a great way come visualize sets. Together is regularly the situation in mathematics, the diagrams took on his name and have due to the fact that taken ~ above his name - Venn diagrams. Because events room sets of outcomes, it functions well to visualize probability as well. Here"s an example of a Venn diagram reflecting two disjoint outcomes, E and also F.


Let"s proceed this a tiny further and put point out on the chart favor this -

- to show outcomes.


Looking in ~ the picture, us can plainly see that P(E) = 5/15 = 1/3, due to the fact that there room 5 outcomes in E, and 15 full outcomes. Similarly, P(F) also is 1/3.

Next, we want to consider all of the occasions that space in either E or F. In probability, we call that event E or F. So in ours example, P(E or F) = 10/15 = 2/3.

But, we can just watch that native the picture! simply count the dots that space E and add to it the number of dots in F.


In general, us can develop a rule. We"ll call it...

The enhancement Rule because that Disjoint occasions

If E and also F are disjoint (mutually exclusive) events, climate

P(E or F) = P(E) + P(F)

Example 1

OK - time for an example. Let"s use the instance from critical section about the household with 3 children, and let"s define the adhering to events:

E = the family members has specifically two guys F = the family members has exactly one boy

Describe the event "E or F" and find its probability.

"E or F" is the occasion that the family members has one of two people one or 2 boys.

Clearly, the isn"t possible for both that these events to happen at the very same time, therefore they room disjoint. The probability of the family having either one or 2 boys is then:

P(E or F) = P(E) + P(F) = 3/8 + 3/8 = 6/8 = 3/4

Of course, there are often instances when two occasions do have outcomes in common, so we"ll require a more robust ascendancy for that case.

General enhancement Rule

What happens when two events do have actually outcomes in common? Well, let"s take into consideration the example below. In this case, P(E) = 4/10 = 2/5, and also P(F) = 5/10 = 1/2, but P(E or F) isn"t 9/10. Have the right to you view why?


The crucial here is the 2 outcomes in the middle where E and also F overlap. Officially, we contact this region the occasion E and F. It"s all the outcomes that room in both E and F. In our intuitive example:


In this case, to uncover P(E or F), we"ll need to include up the outcomes in E through the outcomes in F, and then subtract the duplicates us counted that room in E and F. We speak to this the General addition Rule.

The General addition Rule

P(E or F) = P(E) + P(F) - P(E and also F)

Let"s shot a couple quick examples.

Example 2

Let"s take into consideration a deck of standard playing cards.

Suppose we attract one map at arbitrarily from the deck and define the following events:

E = the card attracted is one ace F = the card attracted is a king

Use these interpretations to find P(E or F).

OK, since E and also F have no outcomes in common, we can use the enhancement Rule because that Disjoint Events:

P(E or F) = P(E) + P(F) = 4/52 + 4/52 = 8/52 = 2/13

Example 3

So the vital idea and the difference between these two instances - once you"re recognize P(E or F), be sure to look because that outcomes the E and F have actually in common.

The match Rule

I think the best method to present the last idea in this section is to consider an example. Let"s look at a deck of conventional playing cards again:

And let"s specify event E = a card much less than a King is drawn. If ns ask you to discover P(E), you"re not going to count them up. (You weren"t walk to, to be you?!) No - you"ll to speak there are 52 cards every together, and there space 4 kings, so thus there need to be 48 cards less than a King. For this reason P(E) = 48/52 = 12/13.

The idea the you"re currently using there is dubbed the complement. (That"s complement, through an e. Not compliment, as in "My, you look pretty today!")

The complement that E, denoted Ec, is every outcomes in the sample room that room not in E.

So essentially, the enhance of E is everything but the outcomes in E. In fact, some messages actually write it as "not E".

How is the complement helpful? Well, friend actually already used the crucial idea in the example above. Let"s look in ~ a Venn diagram.

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From section 5.1, we understand that P(S) = 1. Clearly, E and Ec space disjoint, for this reason P(E or Ec) = P(E) + P(Ec) . Combine those two facts, we get:

The complement Rule

P(E) + P(Ec) = 1

Keep this in mind when you"re feather at an event that"s reasonably complicated. Sometimes it"s much easier to find the probability the the complement.