If the brand new government ads saying “university graduates earn 75 per cent more” have you mentally shouting, “Compared to what?”, or when someone says, “There is not 100 per cent scientific certainty about climate change,” you feel the urge to Kris Kringle them every volume of the IPCC reports, economics professor Dr Gary Smith’s Standard Deviations – Flawed Assumptions Tortured Data and Other Ways to Lie with Statistics is good mental medicine.

An incredibly amusing exploration of how statistics work and how they can be so terribly abused, the book steps the reader through concepts like confirmation bias – how data can be used to confirm an opinion the analyst wanted to see. And it does so in ways that cut through the political spin stats are often given by focusing on eyebrow-raising examples of the misuse of information.

It really sticks in the mind when you’re reading an anecdote like Houdini leaping up from a darkened table unmasking fraudulent mediums or the story of how computers drove a minor stock market panic because of flawed assumptions underpinning the programming.

Yes, there really was a quarry company that claimed having a quarry in the neighbourhood would have a positive impact on property values. There really are people who were funded to research whether the initials DIE actually made someone’s life shorter.

Reading the book raises some interesting questions, like, “How come there are people who get public money to pursue non-essential things like the relationship between baseball results and whether the players have lucky socks or not?”

A very valuable aspect of Dr Smith’s book is that he explains so much of the arcane workings of the stock exchange, including its terminology, and in the process opens some interesting trains of thought about the difference between investors and speculators.

What is valuable for those in the sustainability sector is the clear point that data and theories need to go together – and that empirical data gathered under appropriate conditions is of enormous value in shaping theories. Anything else is… questionable.

Cleverly, Dr Smith steers clear of climate change or other politicised topics, but the example of the quarry project and the conclusions he draws from it are extremely relevant.

Smith reports that the mining company claimed the proposed project would add $172 million in benefits to the local community. However, this figure included $150 million in profits that were going to a foreign-owned company and therefore actually leaving the country.

“Unless there was some vicarious pleasure from watching a corporation get rich, there was no local benefit whatsoever from such profits,” Smith writes.

And the remainder of $22 million, which was attributed to job creation, was in fact a movement of jobs from a neighbouring community to the target community – effectively no new jobs or income overall would be created.

The good news ending is the county refused to approve the project once they properly examined the data, and the big picture message according to Smith is: “Don’t be fooled.”

“We can’t determine if a medicine, treatment, policy or strategy is effective unless we compare it to some alternative. But watch out for superficial comparisons: comparisons of percentage changes in large numbers and small numbers; comparisons of things that have nothing in common except that they increase over time; comparisons of irrelevant data. All of these are like comparing apples to prunes,” Smith writes.

A book on data and statistics that is useful, amusing and informative? It sounds like an oxymoron, but it reads like an intelligent, witty and informed discussion that girds the mental loins for the fight ahead to insist on a proper respect for empirical facts, sound theories and the wise use of data to inform a pathway towards a more sustainable world.

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