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© 2014 Pacific Crest
141
O
ops
! A
voiding
C
ommon
E
rrors
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Wrong distribution
Example
: When modeling data for birth weights of newborn babies, a researcher assumes the
data follows a normal distribution.
Why?
The researcher did not take into account the complications associated with premature
births leading to a skewed distribution.
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Confusing non-cumulative with cumulative
Example
: When comparing a known distribution to data, a student compares relative frequency
with the cumulative distribution and finds they do not match.
Why?
The student must compare relative frequencies to the probability distribution and not
the cumulative distribution.
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Underestimating the scope
Example
: Researchers are determining the ideal distribution of colors for their small size
candies. The company announces they want to hire other researchers to determine
the distribution for the larger sizes.
Why?
The distribution of colors for any size should all be considered part of the same problem
and have the same research team investigating it. Chances are the distributions are
going to be the same.
A
re You Ready?
Before continuing, you should be able to ...
I can...
OR
Here’s my question...
differentiate between cumulative and non-
cumulative distributions
explain the Poisson and normal
distributions (both use and shape)
critically apply the methodology for
modeling a random phenomenon with a
probability distribution
P
lan
How to complete the activity
1. Review the Methodology and Models for Creating Probability Distributions.
2. Answer the Critical Thinking Questions.
3. Complete the remainder of this activity (from Demonstrate Your Understanding through Assessing
Your Performance) on your own, or as directed by your instructor.
3.3 Probability Distributions