Difference between revisions of "Correlation is not causation"
(The difference between science and superstition) |
(how liberals and radical feminists use the fallacy to call for expanded government) |
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| − | '''"Correlation is not causation"''' is the unofficial | + | '''"Correlation is not causation"''' is one of the unofficial mottos of [[modern science]]. When we observe that two things generally or always happen together, it is natural to wonder whether one may be [[causation|causing]] the other. A [[correlation]] between two variables does not always mean that one variable has caused a change in the other. Often a third variable is causing both, such as shark attacks and ice cream sales. These two factors may correlate, but they are not directly related. Rather, both go up each summer as people go swimming at ocean beaches. |
| − | The difference between science and superstition lies in the ways an idea is tested. Scientists test a theory by drawing conclusions from it, hypotheses which can be confirmed or contradicted by observation. | + | It is a [[logical fallacy]] to assume that correlation implies causation. In fact, it is a subset of the [[Logical fallacy#False cause|fallacy of false cause]]. Correlation does not prove causality, but non-correlation proves non-causality.<ref>http://www.oism.org/pproject/s33p36.htm#Message5980</ref> This fallacy is particularly popular with radical [[feminism|feminists]] such as Gail Dines and with other liberals. Liberals and radical feminists use the fallacy to call for expanded government: If there is, or appears to be, a correlation between ''X'' and undesirable event ''Y,'' they argue that ''X'' must be banned to prevent future occurrences of ''Y.'' |
| + | |||
| + | The following is a flawed argument: | ||
| + | |||
| + | #Event A occurs in concurrence with Event B | ||
| + | #Therefore, Event A causes Event B | ||
| + | |||
| + | This is flawed because: | ||
| + | |||
| + | #There may be a third, confounding variable C which causes A ''and'' B | ||
| + | #B may cause A | ||
| + | #The relationship may be a complete coincidence | ||
| + | |||
| + | ==Examples== | ||
| + | |||
| + | 1. | ||
| + | |||
| + | Hypothesis: As sales of ice cream rise, so do reports of shark attacks. | ||
| + | |||
| + | Conclusion: High sales of ice cream cause shark attacks. | ||
| + | |||
| + | '''Flaw:''' Shark attacks and ice cream sales follow a seasonal pattern. Both rise in the summer months. The seasons are a confounding variable. | ||
| + | |||
| + | 2. | ||
| + | |||
| + | Hypothesis: The more people convert to Christianity, the more powerful God becomes. | ||
| + | |||
| + | Conclusion: God gains power from his followers. | ||
| + | |||
| + | '''Flaw:''' God is already all-powerful. His magnificence is what draws people to conversion. B causes A. | ||
| + | |||
| + | 3. | ||
| + | |||
| + | Hypothesis: The construction of a stadium began in Alaska, and the value of your home in Texas has risen. | ||
| + | |||
| + | Conclusion: Stadium construction in Alaska causes higher home values in Texas. | ||
| + | |||
| + | '''Flaw:''' This is a coincidence between unrelated variables. | ||
| + | |||
| + | ==Correlation vs. Causation in Science== | ||
| + | |||
| + | The difference between [[science]] and [[superstition]] lies in the ways an idea is tested. Scientists test a [[theory]] by drawing conclusions from it, [[hypotheses]] which can be confirmed or contradicted by observation. | ||
| + | |||
| + | |||
| + | ===References=== | ||
| + | {{reflist|2}} | ||
| + | |||
| + | *[http://www.stat.tamu.edu/stat30x/notes/node42.html Texas A&M University] | ||
| + | |||
| + | [[Category:Logical Fallacies]] | ||
Revision as of 15:58, September 4, 2019
"Correlation is not causation" is one of the unofficial mottos of modern science. When we observe that two things generally or always happen together, it is natural to wonder whether one may be causing the other. A correlation between two variables does not always mean that one variable has caused a change in the other. Often a third variable is causing both, such as shark attacks and ice cream sales. These two factors may correlate, but they are not directly related. Rather, both go up each summer as people go swimming at ocean beaches.
It is a logical fallacy to assume that correlation implies causation. In fact, it is a subset of the fallacy of false cause. Correlation does not prove causality, but non-correlation proves non-causality.[1] This fallacy is particularly popular with radical feminists such as Gail Dines and with other liberals. Liberals and radical feminists use the fallacy to call for expanded government: If there is, or appears to be, a correlation between X and undesirable event Y, they argue that X must be banned to prevent future occurrences of Y.
The following is a flawed argument:
- Event A occurs in concurrence with Event B
- Therefore, Event A causes Event B
This is flawed because:
- There may be a third, confounding variable C which causes A and B
- B may cause A
- The relationship may be a complete coincidence
Examples
1.
Hypothesis: As sales of ice cream rise, so do reports of shark attacks.
Conclusion: High sales of ice cream cause shark attacks.
Flaw: Shark attacks and ice cream sales follow a seasonal pattern. Both rise in the summer months. The seasons are a confounding variable.
2.
Hypothesis: The more people convert to Christianity, the more powerful God becomes.
Conclusion: God gains power from his followers.
Flaw: God is already all-powerful. His magnificence is what draws people to conversion. B causes A.
3.
Hypothesis: The construction of a stadium began in Alaska, and the value of your home in Texas has risen.
Conclusion: Stadium construction in Alaska causes higher home values in Texas.
Flaw: This is a coincidence between unrelated variables.
Correlation vs. Causation in Science
The difference between science and superstition lies in the ways an idea is tested. Scientists test a theory by drawing conclusions from it, hypotheses which can be confirmed or contradicted by observation.
