In my last post and video, I discussed a bit about the amazing philanthropy and good-will of Bill Gates and in that I mentioned one of the books that Bill Gates rates quite highly entitled, “How To Lie With Statistics” by Darrell Huff.

You can see in the video below his brief reasoning for recommending this book are that it’s “*got a lot of good examples in it, it’s a trip to the past, even though the basic points are still as valid today,*” but I guess the question is what are those points? And why, would want one want to lie when talking about statistics?

I would think that lying in general is not a good thing. The only reason that one would lie, is to not let somebody else know what the truth is and I guess it’s whether that particular lie used was to prevent harm or cause harm would be the difference, in my opinion.

So what would be the reason to lie about statistics, other than to sway someone else’s opinion in a certain direction?

For a man with so much money and power, why would he (Bill Gates) have so much interest in such a book? What is to be gained from the information contained within this book that would attract such attention by this great, philanthropic billionaire?

Well, let’s just have a little look at what this book has to offer before we cast any judgement on Mr. Gates…

Firstly, it may be a good idea to understand a little about the author, Darrell Huff.

According to Wikipedia, Darrell Huff “*is best known as the author of How to Lie with Statistics (1954), the best-selling statistics book of the second half of the twentieth century, ^{}and for his use of statistics as a tobacco lobbyist.”*

^{}

Hmmm…. tobacco lobbyist?…

It goes on to say that “*Stanford historian Robert N. Proctor wrote that Huff “was paid to testify before Congress in the 1950s and then again in the 1960s, with the assigned task of ridiculing any notion of a cigarette-disease link. On March 22, 1965, Huff testified at hearings on cigarette labeling and advertising, accusing the recent Surgeon General’s report of myriad failures and ‘fallacies*‘”

I personally haven’t delved into Darrell’s congressional testimony on this topic, but from the outset, it would seem that Darrell may of been using his great skills in lying about statistics to possibly lobby a product that was knowingly causing harm to others, as it is common knowledge today that cigarettes are harmful to one’s health.

The front cover of the book is just a fellow sweeping numbers under a rug. Nothing to see here!

Considering that this book is one of the best selling statistics books of the twentieth century, one must wonder how many people have successfully learnt the art of lies and deceit through statistics with the help of Mr.Huff’s knowledge.

In the intro pages of this book Darrell states that, “*This book is sort of a primer in ways to use statistics to deceive. It may seem altogether too much like a manual for swindlers. Perhaps I can justify it in the manner of the retired burglar whose published reminiscences amounted to a graduate course in how to pick a lock and muffle a footfall: The crooks already know these tricks; honest men must learn them in self-defense*.”

It seems from this statement that he is trying to make it seem like his motives for writing this book are so that “honest men” can learn the tricks of the trade, but considering his known background of lying about tobacco products causing harm, we can only assume he is probably more like the “retired burglar” giving his secrets away.

And a nice little quote from the paragraph above, “*Like the little dash of powder, little pot of paint, statistics are making many an important fact “look like she ain’t.” A well-wrapped statistic is better than Hitler’s “big lie”; it misleads, yet it cannot be pinned on you.*“

I don’t exactly know what Darrell is referring to when he mentions Hitler’s “big lie” but we can already start to understand that this technique of lying with statistics is generally not going to be used for good reasons.

Much of what I can understand from this book, is that it’s not so much about directly *lying* about a statistic *per se, *ie fudging numbers, but more so about how the information is presented or packaged which can affect your perception or view on a particular topic.

The book gives an example on Pg. 32, “*The United States Corporation once said that its employees average earning weekly earnings went up 107 per cent between 1940 and 1948. So they did – but some of the punch goes out of the magnificent increase when you note that the 1940 figure includes a much larger number of partially employed people. If you work half-time one year and full-time the next, your earnings will double, but that doesn’t indicate anything at all about your wage rate*.”

This information is correct and may give the *impression* that wages rates have increased, when in fact the only thing that has happened is that people have been working more hours, therefore their earnings have doubled. Quite often, it’s the information that is omitted from a statistic or piece of information that we really need to know, to fully understand the context.

The wording in describing certain statistics is probably more important than the statistic itself.

On Pg.66, the book states, “*There are many ways of expressing a figure. You can, for instance, express exactly the same fact by calling it a one per cent return on sales, a fifteen per cent return on investment, a ten-million-dollar profit, an increase in profits of forty per cent (compared with 1935 average), or a decrease of sixty per cent from last year. The method is to choose the one that sounds best for the purpose at hand and trust that few who read it will recognize how imperfectly it represents the situation.*“

All of these statements mean the same thing, but result in a different way to think of them and can be spun in a way to swing your opinion. It’s quite clever really…

Another example the book gives on its deceptive techniques is that corporations or purveyors of statistical information can use a small sample size of data when they give their stats. This can allow these corporations to run many tests or surveys until they get the result that they want to publish. Therefore, technically, they would not be lying.

If you were to flip a coin 10 times and heads comes up 8 times, then you could say with certainty that heads comes up 80% of the time. If you were not happy with that result, you could do this test again with the same amount of flips and get a different result and continue this until you get the desired result.

Again, technically speaking, this would not be a lie, but of course a larger sample size would give a much broader and more accurate representation of the particular test or survey on a particular topic.

In Darrell’s chapter, “*Post Hoc Rides Again*“, he describes something known as the “*post hoc fallacy*” in which it is said that one event that has occurred has caused a later event.

Darrell gives this example: “*Somebody once went to a good deal of trouble to find out if cigarette smokers make lower college grades than non-smokers. It turned out they did. This pleased a good many people and they have been making much of it ever since. The road to good grades, it would appear, lies in giving up smoking; and, to carry the conclusion one reasonable step further, smoking makes dull minds.*“

This gives the assumption that event A (smoking) has caused event B (bad grades), but could it not be the other way around? “*Perhaps low marks drive students not to drink but to tobacco*” as the book states. Or could there be a third factor which causes these results? Well, that would depend on how you wanted someone to feel about this information.

It’s all about the deceptive use of language to push a message or point of view based on certain statistics and information, in the way that you want it to be perceived by a viewer.

The use of charts and graphs to display certain information can also be used as a deceptive way to help push a message to the public and influence opinion and this can be done in a very similar fashion to the use of language.

The graph below shows a 10% increase in national income over the course of a year. Apart from the smug businessman in the pic, it shows this increase, however, it does not look like a very overwhelming incline.

All that’s needed to make this graph more dramatic and substantial, is to “*simply change the proportion between the ordinate and the abscissa. (x & y axis) There’s no rule against it, and it does give your graph a prettier shape,*” this great book suggests…

From a visual perspective, the second graph gives a much more dramatic and influential representation of the same information so that the impact is greater to the reader.

There are more example and ideas given in this book but overall, we can see from this brief look at “How To Lie With Statistics,” the content is about using nothing other than deception and lies in order to influence opinion on a particular thing.

The techniques from this book will almost never be used for good reasons, as I can’t really think of too many reasons why an honest person would want to lie about such things, other than for personal gain.

Considering that Bill Gates is someone who uses a lot of statistics and charts in his presentations, it might be something to keep in mind that one of his favourite reads, is this Darrell Huff best-seller, “HOW TO LIE WITH STATISTICS!!!”

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