1.6 Correlation Does Not Equal Causation

In the previous example, you may have selected “Oral contraceptive usage is correlated with cervical cancer”. This statement is accurate and does not imply that using the Pill necessarily leads to cervical cancer. Nor do we have any reason to think that Brinton’s study was flawed. She used a relatively large number of participants to look for trends, and did her best (using randomly generated phone numbers) to find a random sample for her control group. However, it is understandable that, when this study (and others like it) came out, many people were tempted to think that using oral contraceptives led to cancer. These individuals were falling victim to the idea that correlation implies causation; this misconception is powerful and has created a lot of trouble for scientists.

Note that some correlations are positive: increase/decrease in one variable is correlated with increase/decrease in the other variable. For example, there is a strong correlation between decreasing divorce rates in Maine and decreasing consumption of margarine(Figure 1.4):

Figure 1.4 Divorce rates in Maine correlates with per capita consumption of margarine. 
Figure 1.5 Per capita consumption of mozzarella cheese correlates with Civil engineering doctorates awarded. 
Note the source! The above two examples are from a [fun!] website called “spurious correlations,” so they aren’t meant to be taken seriously, however the data are real: http://www.tylervigen.com/spurious-correlations
Figure 1.6 Absences versus percent score

Some correlations are negative, whereby an increase in one variable is correlated with a decrease in the other.  The data above compare course absences with percentage performance in the course.

  Check Yourself



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The Evolution and Biology of Sex by Sehoya Cotner and Deena Wassenberg is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, except where otherwise noted.