The probabilities of certain events may change as a result of earlier events. For example, if it rains today, the probability that it rains tomorrow may be greater than if it was sunny today. When using a tree diagram, check that the probabilities do not change as a result of the first event. When probabilities depend on previous events they are called conditional probabilities.
Worked Examples
A bag contains 7 red balls and 3 green balls. Two balls are taken from the bag. Find the probabilities that they are:
both red
both green
one red and one green.
For the first ball taken from the bag,
p(Red) = and p(Green) =
If the first ball was red the bag now contains:
6 red balls and 3 green balls
so
| p(Red) | = | and | p(Green) | = |
| = | = |
If the first ball was green the bag now contains:
7 red balls and 2 green balls
so
p(Red) = and p(Green) =
These probabilities can be used to create a tree diagram.

So
| p(both red) | = |
| p(both green) | = |
| p(one red and one green) | = + |
| = | |
| = . |
Weather experts estimate the probability of rain any day as
0.6 if it rained the previous day
0.3 if it did not rain the previous day.
Find the probability that a dry day is followed by,
two more dry days,
two wet days,
a wet day and then a dry day.
The tree diagram has the probabilities included on its branches. Note how they depend on the weather on the previous day.

p(two more dry days) = 0.49
p(two wet days after the dry day) = 0.18
p(dry followed by a wet day and then a dry day) = 0.12

