REPEAT AFTER ME:
Correlation is NOT causation!
Correlation is NOT causation!
Correlation is NOT causation!
Correlation is NOT causation!
 Correlation shows how two
variables relate together.
 It is often confused that
correlation can show a cause
 Examples: ACT scores
correlate to college success (or
failure)
 Parents who have children
before the age of 18 are more
likely to have children who
have children before the age of
18
 Correlations may be influenced
in either direction
 Example: Friends tend to dress
and act the same. Is it because
of how they dress that they are
friends or is it that because
they are friends they choose to
dress and act similar?
 With Correlation, one can never
tell.
Graph showing illusory correlations
 Correlations measure the
STRENGTH of a relationship
 Correlation coefficient (r value)
 Between 1.00 to zero to -1.00
 The closer to 1.00 or -1.00, the
stronger the relationship
 The closer to zero, the weaker the
relationship.
 The +/- indicates the direction of
the relationship
 Which of the following
correlation coefficients represents
the STRONGEST relationship?
-.87 .64 -.32 .24
 Which is the weakest?
 Correlations are represented
visually through scatter plots
 Scatter plot – a cluster of dots,
with each dot showing the values
of two variables
Each dot in the instance above
shows a husband’s age (x-axis)
and his wife’s corresponding
age (y-axis)
 Correlations are either
positive or negative
 Zero correlation = no
relationship exists – such as
age and eye color
 Positive: As one variable
Which correlation is positive, and
increases, the other
which is negative in the examples
increases (and vice versa)
above?
 Negative: As one variable
Type of Correlation Change in variables
increases, the other
Increase, Increase
decreases (and vice versa) Positive
 In math terms:
Positive
Decrease, Decrease
 positive correlation = direct
relationship
 negative correlation =
inverse relationship.
Negative
Increase, Decrease
Negative
Decrease, Increase
Video Clip: The Joy of Stats
 Correlation only shows that TWO
variables relate to each other, but
most issues are far more complex
 Intervening variables – a variable
that may explain a correlation’s
existence
 EX: Education and income are
positively correlated, but does
education actually provide any
income?
 The intervening variable in this
case is a job. An education
provides a chance at a better job
and better jobs pay more which
leads to more wealth
 So, the reason for education and
income’s correlation is job
opportunity
 Illusory correlations - The
perception of a relationship
between two variables where none
exists
 Example: There is more crime on a
full moon
 Example: My horoscope can predict
my future
 Here’s why this is so convincing. In
psychology, we’ll study a concept
called confirmation bias and it is the
root of all evil. It states that as
people we always look for things
that confirm our belief and dismiss
things that contradict that belief.
 This is called Type I error. The
reality is false, but we perceive it as
true
Both of the above examples are not
true; study after study has shown
that no more crime happens on a full
moon than any other moon phase
and numerous studies have
confirmed that horoscopes have no
predictive value. But Type I error is
SO powerful that these ideas remain.
 Extraneous (or 3rd) variables –
Sometimes a correlation does not
exist at all but it appears that it does
because of a third variable.
 There is a positive correlation
between ice cream and murder rates
 Does that mean that ice cream
causes murder?
 What does ice cream and murder
have in common?
 Video Clip: Correlation vs. Causality
(poor ice cream getting a bad rap)

People who eat Frosted Flakes as children had half the caner rate of those who never ate
the cereal. Further, children who ate oatmeal as kids had 4x the cancer rate than those
who did not.


This is my favorite correlation story! In the early twentieth century, thousands of
Americans in the South died from pellagra, a disease marked by dizziness, lethargy,
running sores, and vomiting.


Illusory correlation: Frosted Flakes was not invented until 1951, at the time of the study,
those who ate frosted flakes were not likely to get cancer because they weren’t old enough
yet (cancer is correlated with age)
Finding that families struck with the disease often had poor plumbing and sewage, many
physicians concluded that pellagra was transmitted by poor sanitary conditions. In
contrast, Public Health Service doctor Joseph Goldberger thought that the illness was
caused by an inadequate diet. He felt that the correlation between sewage conditions and
pellagra did not reflect a causal relationship, but that the correlation arose because the
economically disadvantaged were likely to have poor diets as well as poor plumbing.
How was the controversy resolved?
The answer demonstrates the importance of the experimental method. He selected two
patients—one with scaling sores and the other with diarrhea. He scraped the scales from
the sores, mixed the scales with four ml of urine from the same patients, added an equal
amount of feces, and rolled the mixture into little dough balls and he, his assistants, and
his wife ate them. None of them came down with pellagra.
 He then conducted another experiment on prison inmates who either got a balanced diet
or a bad diet. The bad diet inmates all got pellagra and the balanced diet inmates did not.
