How to Lie with Statistics

Statistics is hard.  Let’s all go to the beach.
-Barbie

You know, I really enjoy being an information analyst.  Statistics has been a very rewarding career choice.  Over time I’ve learnt to swim through data like a fish dives through water.  In fact, remove me from statistics and I’d probably flap around gasping to breathe just like a landed fish.  But after many years I’ve come to accept that the vast majority of the population simply don’t “trust” statistics.  I admit not without good reason.  On the one hand we’re bombarded with statistics every day, mostly from the media (both “as reported” and by advertising).  On the other hand statistics are too often twisted, corrupted, misrepresented, biased, misused, falsified, misreported or sometimes simply ignored (not by me of course, heh).  No wonder some people throw up their hands and declare it’s all too hard.  Why bother paying attention anyway when 83.7% of all statistics are simply made up on the spot?

With that in mind I’ve just finished reading How to Lie with Statistics by Darrell Huff.

I understand that How to Lie with Statistics is one of the, if not the highest selling books on statistics ever written.  An extraordinary achievement, especially considering Huff had no formal training in statistics.  The concepts are all too familiar to me, but of course How to Lie with Statistics is not aimed at the professional.  It’s very much an introductory text aimed squarely at a non-technical audience.  My copy was a mere 124 pages long, making How to Lie with Statistics something that can be digested in just a couple of hours.  First published in 1954 it’s striking how, even though some of the language has dated terribly (“Negro”? “Mongolism”?), the basic ideas expressed inside are timeless.  Warning people to beware of such things as hidden bias, inappropriate sampling, “conveniently” omitted details, and inappropriate measures (e.g. using mean when median is more appropriate) remain as relevant in 2009 as in 1954.  They’ll still be relevant in 2059.

Duff certainly writes entertainingly and with good humour throughout, making How to Lie with Statistics a very accessible and enjoyable read.  More than 50 years after first being published, many of the statistical “sins” highlighted by Huff in his book are still being committed today.  By way of example – correlation being used to imply causation, graph scales used to exaggerate minor differences and “OK names” being used to mask dodgy sources.  In conclusion, How to Lie with Statistics will help the average reader identify the various statistics “sharks” that can lurk in these waters.

Safe swimming.

In future blog entries I’d like to expand further on some of the concepts that Huff wrote about in How to Lie with Statistics, hopefully using some real world examples.

Further reading:

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7 Responses

  1. Just passing by.Btw, you website have great content!

    • Thanks!

  2. You make a good point about statistics being “hard”. One fundamental issue, though, is that statistical summaries and analysis attempt to make sense of a complicated world. If our reality is complex, we cannot expect statistics (or any other analysis) to reduce that complexity beyond a certain point.

    Many people are unwilling to expend the mental effort required to understand said complexity. In my opinion, this is not the fault of statisticians or their work product. The audience has some responsibility to actually care enough to learn about the issues. History has shown in many fields (war, medicine, race relations, finance, etc.) that when people do not adequately understand their environment, they suffer for it.

  3. FANTASTIC!

  4. Lots of of people blog about this matter but you said some true words.

  5. good point

  6. I had Statistic in my BA… just basic theoretical statistics, it was mandatory in a range of study programmes. The first 6 weeks of the subject was a crash course named ‘How to lie with Statistics'; in which the lecturer analysed misrepresented Statistics data that had been quoted in the media and had us calculate why it was misrepresented/misunderstood/misquoted by the journalists.

    A great start that gave meaning to the (whining) question: ‘Why do we need to understand Statistics?’… with the answer -‘Because otherwise you will be fooled by it!’

    Ps. Great blog!

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