Assessing the risk of an asset requires that we
have some sense for the range of possible outcomes.
For example, a judge sentencing a youthful offender
might consider the likelihood of different scenarios:
 worst case (pessimistic), the offender will
commit only another minor crime;
 expected case (normal), she will commit no
more crimes
 best case (optimistic), she will prevent others
from committing crimes
Given this distribution, the judge might suspend
the youthful offender's sentence.
Predicting a range of outcomes and assigning
to them different probabilities can give further
insight into risk. Using a probability distribution,
we can model different outcomes. For example,
the probability distribution of the results of
throwing two dice is shown in the table to the
right  
Example
Result 
Probability 
2 
1/36 = 2.8% 
3 
2/36 = 5.6% 
4 
3/36 = 8.3% 
5 
4/36 = 11.1% 
6 
5/36 = 13.9% 
7 
6/36 = 16.7% 
8 
5/36 = 13.9% 
9 
4/36 = 11.1% 
10 
3/36 = 8.3% 
11 
2/36 = 5.6% 
12 
1/36 = 2.8% 
Total 
36/36 = 100% 
The most likely throw is a 7 
but it happens only onesixth of the time. Possible
outcomes range from 2 to 12, although the likelihood
of throwing a 2 or 12 is significantly lower
than throwing a 7. What if you wanted to throw
at least a 7? You would have to consider variability
in assessing risk. Even though 7 would be the
most frequent throw, you might throw less. In
fact, there is a 41.6% chance that you will
throw less than a 7.
