In this eye-opening book, author Lee Baker uncovers the key tricks used by statistical hustlers to deceive, hoodwink and dupe the unwary.Written as a layman’s guide to lying, cheating and deceiving with data and statistics, there’s not a dull page in sight!Discover the exciting world of statistical cheating and persuasive misdirection. How To Lie With Statistics. By Darrell Huff. Benjamin Disraeli: “There are three types of lies: lies, damned lies, and statistics”. In the 1950s, when this book was written, Britain was considering changing over from the Imperial measurement system to the Metric System.

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Benjamin Disraeli: “There are three types of lies: lies, damned lies, and statistics”

In the 1950s, when this book was written, Britain was considering changing over from the Imperial measurement system to the Metric System. Obviously that’s a pretty massive change, to shift from miles to kilometers, pounds to kilos, and from measuring things in ancient, archaic, out-dated ways to measuring in a more standard and modern way. At the time this was considered to be a huge shift (today there are only three countries stuck in the Imperial past that haven’t got with the times… Myanmar, Liberia, and world’s largest economy, America).

Gallup surveyed the Britons and found that 33% of people didn’t even KNOW what the metric system was, let alone how to use it or how it might become the country’s new standard for weights and measures. The Sunday newspaper was outraged at this statistic, so they conducted their own survey, and found that 98% of their readers DID know exactly what the metric system was and what its implications were for the country. How could two survey end up with a 16.5X result? One saying 33% were unaware, one saying only 2% were unaware. This is clearly not a small error, this is How To Lie With Statistics.

The Gallup poll was conducted by a reputable organisation that does these kinds of surveys all day every day. They conducted a rigorous assessment of a broad cross section of the population. The Sunday paper had their own interests in mind, and conducted a different type of survey. In order to be included in the Sunday paper’s survey, firstly you had to obviously read the Sunday paper. Also, you had to cut out half of a page, answer the questions, then send it back. That takes a special type of person (apologies if you regularly respond to newspaper surveys… but if you do, you’re probably not listening to our podcast or reading our blog posts anyway). One argument the newspaper might make is that well-educated people read their publication, which is why they scored so high. Another argument though – if you DIDN’T know what the metric system was, why would you bother with the time and effort to snip out and survey and spend money to mail it in? If you had a big ego and didn’t want to admit that you didn’t know something, you’d probably go and research what the metric system was, then send in the survey saying “YES”. All of these things contribute to skewed data and dodgy stats. This is how to lie with statistics. It’s not surprising they ended up with completely different results.

This is obviously a somewhat trivial example, but lies with statistics are more common than you realise. Any time you see a statistic, you should consider that it might be a lie.

You need to learn some of these tricks so that you can defend yourself. The author says that the crooks know exactly how to manipulate data and statistics, so the honest people must learn the tricks as a matter of self defense. (Or, if you’re that way inclined and a little sneaky, you can sue some of these tricks to manipulate data of your own and lie with statistics a little more).

In the podcast episode and in this blog post, we’re going to cover:

Sampling Errors
Biased Sampling
The different types of “averages”
How you can use and unrelated number to answer a different question
And How you can fight back against these dodgy statisticians

BIASED SAMPLING

Let’s say you’ve got a barrel full of beans. Some are red, some are white. If you’re a pedantic person, like Ashto, you might want to know exactly how many beans of each colour you have. The only way to be completely certain about the exact figure is to top them all out and count them one-by-one. That’s a bit of a pain in the arse, and it’d take a pretty long time. So you want to find a short-cut, a way to get a good-enough estimate that you can be pretty confident in:

You plunge your hand into the barrel and grab a bunch of beans.
You count how many in that handful are red and how many are white.
You extrapolate these proportions to make an educated guess about the whole barrel (by estimating the number of handful there are in the barrel, you can multiply out the number of beans of each colour in your hand)

Of course, if all the whites are at the bottom and all the reds are at the top, then scraping a handful off the top isn’t presentative of the whole barrel. And if you’ve got tiny hands and can only pick up one or two beans, you’re not getting a good enough sample size. But if you’ve got a big enough handful, and that handful is ‘representative’ of the whole barrel, then you’ll get pretty close to an accurate estimation.

One extreme example of how to lie with statistics through sampling errors is a questionnaire asking people: “do you like answering questionnaires?”. The researcher found that almost 100% of people like to answer questionnaires!! What an incredible result!!?! But of course… the only people who are going to answer that questionnaire… are people who like answering questionnaires! Your sample has effectively eliminated anyone that might say “no” to this questionnaire, so your results are heavily biased. Similarly, psychiatrists might say that “pretty much everyone is crazy”, but remember who is in the sample of people going to see a psychiatrist (note that this book was written in the 1950s, some of these examples didn’t hold up that well over time…). Another one that doesn;t stand that test of more politically-correct times: a plastic surgeon saying that almost all women have weird, small boobs.

SO… The results you get from a sampling study is only as good as the sample that it is based on. Unless the sample is truly “representative” of the population as a whole, from which every potential source of bias has been removed, then the study is effectively useless. This is NOT to say that a sample has to be an exact match of the entire population: it doesn’t have to be 50% men 50% women, the exact splits of religions, incomes, number of people in their family, level of education, etc etc etc. It doesn’t mean that every element of society is IN the study, just that everyone has an equal CHANCE to be a part of the random sample.

Cherry-Picking The Stats You Want

“If you can’t prove what you want to prove, demonstrate something else and pretend that they are the same thing”. Wise words of advice from the author Darrell Huff. “In the daze that follows the collision of statistics with the human mind, hardly anybody will notice the difference. The semi attach

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