Truth, Damn Truth, and Statistics


Authors: 
Paul F. Velleman
Volume: 
16(2)
Pages: 
online
Year: 
2008
Publisher: 
Journal of statistics education
URL: 
http://www.amstat.org/publications/jse/v16n2/velleman.html
Abstract: 

Statisticians and Statistics teachers often have to push back against the popular impression that Statistics teaches how to lie with data. Those who believe incorrectly that Statistics is solely a branch of Mathematics (and thus algorithmic), often see the use of judgment in Statistics as evidence that we do indeed manipulate our results.<br><br>In the push to teach formulas and definitions, we may fail to emphasize the important role played by judgment. We should teach our students that they are personally responsible for the judgments they make. But we must also offer guidance for their statistical judgments. Such guidance requires that we acknowledge the role of ethics in Statistics. The principle guiding these judgments should be the honest search for truth about the world, and the principle of seeking such truth should have a central place in Statistics courses.<br><br>The remark attributed to Disraeli would often apply<br>with justice and force: "There are three kinds of lies:<br>lies, damn lies, and statistics".<br>-Mark Twain<br><br>This may be my least favorite quotation about Statistics. But I wish to address what underlies both the quotation and the gleeful willingness of many who know nothing at all about Statistics to quote it as if it justified their low opinion of the discipline.<br><br>This quotation has infiltrated discussions in many disciplines. Surely you have had it quoted back to you if you were foolish enough to admit in polite company that you teach Statistics. Nigel Rees's Quote...Unquote1 claims that this is the single most quoted remark in the British media.2 A Google books search of "lies, damn lies, and statistics" turns up 495 books, and a general Google search finds "about 207,000" hits. A small (nonrandom) sample of these references shows that most are meant to suggest dishonest manipulations and interpretations.

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education