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H.G. Wells once remarked, “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.”
In the 19th century, Statistics had started to gain prevalence. Statisticians were now using probability theory in their research projects and their clout was growing.
While statistical obsession was catching on, so was the number of charlatans who sensed an immediate opportunity from the deployment of statistics.
Darrell Huff, in 1954, decided to equip the ordinary folks with handy information about statistical pitfalls. “How to lie with Statistics” is Huff’s compass to a layman to keep the number-wielding phoneys at bay.
In Huff’s own words, “The crooks already know these tricks; honest men must learn them in self-defense.”
Almost six decades on since the first print and due to lack of any revised editions, many examples in the book sound outdated. Illustrations also look below-par work of a mediocre graphics artist and even some of the well-intentioned advice sounds commonplace.
Despite these deficiencies, the book seems to have stood the passage of time.
Its relevance for anyone who wants an initial peek into the world of statistics can’t be overstated. Let’s face it. The flurry of data-laden information coming our way has shot up manifold since ’50s. And, with so much data floating around, the statistical chicanery has gone up several notches, too.
One of the basic traps in statistics, according to Huff is the built-in sample bias. A sample is biased when it doesn’t adequately represent the population from which it is drawn. Determining a truly random sample is a strenuous task yet everyone claims to find their fit.
If a Business school publicizes its mean average salary without ever mentioning the ‘median’ salary, then it’s likely to be hiding something. Remember, one extraordinarily high figure can pull up the mean average salary by a long shot whereas a median figure depicts a much better picture as to how the B-school has actually fared in placing its students.
The vested interests intentionally deploy these traps. There are also times when such pitfalls inadvertently sneak into the picture doing even more damage.
Huff mentions several other tricks that advertisers and propagandists often use to flummox consumers and sway trends. The deliberate use of small samples to exaggerate outcomes, intentional deployment of pictorial graphs that bear no resemblance to the real situation are some of the other con-techniques that Huff discusses in the book.
In a chapter titled ‘The Gee-Whiz Graph’, he illustrates how, sometimes, graphs are twisted and molded so as to impart an entirely opposite meaning without falsifying anything.
Let’s say you have to make a presentation to a bunch of media newbies. Your boss instructed you to include quarterly trends of last one year. Now, this graph with dips in July-Sept and Jan-Mar quarters paints a rather grim picture (Graph 1). Whereas your objective is to put an optimistic picture to your audience. So what do you do?
You manipulate the graph without falsifying the data. Instead of using Quarterly spends, you plot the same data in terms of cumulative media spends (Graph 2). Which picture looks rosier, you decide!
Huff aptly quips later in the book, “There are often many ways of expressing any figure…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 reflects the situation.“
In the later chapters, Huff sheds light on the dubious world of percentages and fractions. He discusses some overdone gimmicks such as ‘small base‘ effect, used to highlight disproportionate gains except that gains are anything but disproportionate.
At the same time, there are certain ruses that continue to escape public scrutiny. One such ruse is the difference between percentages and percentage points. If your profits on investment have increased from 3% last year to 6% this year, that’s a jump of 3 percentage points. But if you want to impress someone, you can term it as a 100% increase.
“How to lie with Statistics” adds no more bewilderment to an already perplexed world of numbers. Huff’s advice is simple: switch on your critical faculties before accepting any research-based conclusion.
He avoids the wonkish stuff and instead builds his work around the common traps in the statistical world.
The fact that it’s a recommended book in some of the undergraduate courses around the world is in itself a grand testimony to its relevance.
I strongly recommend this book to those who have a tangential knowledge of statistics. It is an old-fashioned, honest manual – the title notwithstanding – on statistical artifices that has withstood the knocks of time and is still as pertinent as it was 60 years ago.