Spurious Correlations

    
      Tyler Vigen, a student at Harvard Law School and the author of Spurious Correlations, has made sport of spurious correlations on his website which charts farcical correlations.
     While his charts are comical, some are not so much. In statistics, a spurious relationship or spurious correlation is a mathematical relationship in which two or more events or variables are not causally related to each other, yet it may be wrongly inferred that they are, due to either coincidence or the presence of a certain third, unseen factor.
     Biases and conditions alone may create statistical errors, errors from interpretation, false reporting, wrong assumptions, improper samples, false relationships and even bad math. You can prove or disprove just about anything.

Common occurrences of spurious correlations include:
Apples and Oranges Comparing Dissimilar Variables - Y axis scales that measure different values may show similar curves that shouldn’t be paired. This becomes pernicious when the values appear to be related but aren’t.
Skewed Scales Manipulating Ranges to Align Data - Even when Y axes measure the same category, changing the scales can alter the lines to suggest a correlation. In these charts the left Y-axis is different increment than the right Y-axis.
Ifs and Thens Implying Cause and Effect - Plotting unrelated data sets together can make it seem that changes in one variable are causing changes in the other.

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