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Q
uantitative
R
easoning &
P
roblem
S
olving
440
© 2014 Pacific Crest
O
ops
! A
voiding
C
ommon
E
rrors
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Incorrect sampling
Example
: A simulation uses each of the possible input values equally and results in false
predictions.
Why?
The input follows a probability distribution that is not uniform, so sampling must be
done according to the specified distribution.
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Not enough iterations
Example
: A simulation is run with each of the possible probability distributions and no pattern
is apparent in the outputs.
Why?
Many simulations require large numbers of iterations in order for patterns to reveal
themselves.
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Limitation of simulation
Example
: A medical student trains with simulations and becomes certain that he or she can
therefore deal with any problem.
Why?
No matter how detailed a simulation gets there are situations in real life that cannot
be predicted. It is important to keep in mind the limitation of simulations.
A
re You Ready?
Before continuing, you should be able to ...
I can...
OR
Here’s my question...
explain the difference between mathemati-
cal modeling and simulations
discuss the role of probability distributions
in simulations
explain understand the steps of the meth-
odology for running simulations
P
lan
How to complete the activity
1. Review the Discovery and Information portions of this activity.
2. Examine the Models.
3. Download the spreadsheets with examples of converting static models to simulations (available as
Resources on the companion website).
4. Discuss and answer the Critical Thinking Questions.
5. Complete the remainder of this activity (from Demonstrate Your Understanding through Assessing
Your Performance) on your own, or as directed by your instructor.