Question

This week we're exploring causation and correlation. Why is it a fallacy to confuse causation and correlation? Provide an example of a statement that confuses causation with correlation.

Answer #1

I am going to answer this question in three parts

**1)Correlation is a statistical measure (expressed as a
number) that describes the size and direction of a relationship
between two or more variables.** A correlation between
variables, however, does not automatically mean that the change in
one variable is the cause of the change in the values of the other
variable.

Causation indicates **that one event is the result of the
occurrence of the other event; i.e. there is a causal relationship
between the two events. This is also referred to as cause and
effect.**

2)In statistics, the phrase "correlation does not imply causation" refers to the inability to legitimately deduce a cause-and-effect relationship between two variables solely on the basis of an observed association or correlation between them.

3) Example, the number of drownings and sales of ice creams may
be highly **correlated but** that doesnt imply one
causes the other. Drownings and sales of ice cream are obviously
higher in the summer months when the weather is good. Third
variable aka good weather causes them.

Explain how and why Correlation is NOT Causation.

What is the difference between correlation and identity?
Why is it important to distinguish between the two concepts?
Provide an example of a statement that confuses correlation with
identity.
In addition to your main response, you must also post substantive
responses to at least two of your classmates’ posts in this
thread.

The adage in psychology is that “correlation does not imply
causation.” However, we know that smoking cigarettes causes cancer,
despite the fact that most of the evidence for this is
correlational. Provide an example of a hypothetical study where
correlation does not imply causation and what the researcher can do
to further understand the nature of the correlation.

Explain why correlation doesn't necessarily mean causation on a
scatter plot.

We have learned that “correlation is not causation.” However, we
have also learned that correlational studies can provide evidence
for hypothesized causal mechanisms. There are three rules for
establishing causation between two variables. What are these rules?
Give an example for each of these rules.

1. Clearly explain the difference
between correlation and causation. Explain which
type of connection is harder to prove and why. Provide two clear
examples to illustrate the differences between the two
concepts.

How might squaring a correlation coefficient be useful to
understanding the relationship between two variables?
Why is it important to remember “association, not causation”
when discussing correlations? Please provide an example.

Why is it important to remember “association, not causation”
when discussing correlations? Please provide an example.

Discussion 1: Searching for Causes
This week examines how
to use correlation and simple linear regression to test the
relationship of two variables. In both of these tests you can use
the data points in a scatterplot to draw a line of best fit; the
closer to the line the points are the stronger the association
between variables. It is important to recognize, however, that even
the strongest correlation cannot prove causation.
For this Discussion,
review this week’s Learning Resources...

So, as we look at Linear Regression and correlation this week,
please find provide an example of how and when linear regression is
used.

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