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Six Ways to Separate Lies From Statistics

Posted By Barry Ritholtz On May 26, 2014 @ 1:00 pm In Bad Math,Data Analysis,UnScience | Comments Disabled

From Betsey Stevenson & Justin Wolfers, a short primer on separating lies from statistics:

1. Focus on how robust a finding is, meaning that different ways of looking at the evidence point to the same conclusion. Do the same patterns repeat in many data sets, in different countries, industries or eras?

2. Results that are Statistically Significant means it’s unlikely findings simply reflect chance. Don’t confuse this with something actually mattering.

3. Be wary of scholars using high-powered statistical techniques as a bludgeon to silence critics who are not specialists.

4. Don’t fall into the trap of thinking about an empirical finding as “right” or “wrong.”

5. Don’t mistake correlation for causation.

6. Always ask “so what?” The “so what” question is about moving beyond the internal validity of a finding to asking about its external usefulness.

Great stuff. I recall something from Carl Sagan on this — I’ll see if I can dig it up.


Six Ways to Separate Lies From Statistics [1]
By Betsey Stevenson & Justin Wolfers
Bloomberg View, May 1, 2013   http://www.bloombergview.com/articles/2013-05-01/six-ways-to-separate-lies-from-statistics

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[1] Six Ways to Separate Lies From Statistics: http://www.bloombergview.com/articles/2013-05-01/six-ways-to-separate-lies-from-statistics

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