What does excess email usage have to do with low IQ scores? Nothing at all, but that doesn’t mean someone didn’t create a connection to make a point.
I don’t know about you, but I am tired of the incorrect, misleading, or just plain bogus statistics used to sell me a product, elicit support for a candidate, or get me to ‘Like’ some new trend. I’m mad as hell and I’m not going to take it anymore...and neither should you.
Misleading with statistics is called ‘statisticulation’ and it is nothing new. In 1954, former Better Homes and Gardens editor Darrell Huff wrote a small book called, How To Lie with Statistics, which is the best-selling statistics book of the last 60 years, according J. Michael Steele, a professor of statistics and operations and information management at Wharton.
What was true in 1954 is just as true today. According to Huff, here are seven common tactics used to knead statistical data into "dough."
- Biased sampling: This involves polling a non-representative group. For example, a survey that finds "41% of retail bank customers would use mobile banking if it were available," becomes meaningless when you find out the survey only polled people on their mobile devices.
- Small sample sizes: Picking an adequate sample size is part science and part art, but sweeping statements, like "14% of companies plan to deploy cloud-based email this year" becomes suspect when the sample size is 24 companies. Another example of this kind of ‘statistics gone wild’ phenomenon was a "study" conducted by HP that found excessive email usage reduces a person’s IQ by 10 points.
- Poorly-chosen averages: This often involves averaging values across non-uniform populations. For example, I recently saw an article that identified a neighborhood as one of the wealthiest in the city. The article went on to state that neighborhood residents had an average annual income of around $100,000. What the article failed to point out is that the neighborhood is in the process of gentrification; one part of the neighborhood is very wealthy, and the other part’s income levels are below the national average. Giving a single average value for two populations is incorrect and misleading. (The median value for income would be a better indication of the neighborhood income.) Another classic example of this is the story about the man who drowned in a pool of water whose average depth was 1 inch.
- Results falling within the standard error: No sampling or measuring technique is perfect; all inherently incorporate a degree of error. This means that a survey can only be as accurate as its standard error. Without getting technical, suffice it to say that the headline, "E-books Preferred Over Paper By Men More Than By Women" sounds remarkable until you find out that of the actual polling results found that 52% of men preferred e-books versus 49% for women, and the error of the survey was +/-5%.
- Using graphs to create an impression: Both of the charts below contain exactly the same information, but which one more accurately shows the increase in venture capital investment in mobile technologies between the years 2006-2007? The only difference between the graphs is the scale. Graphing data creatively provides a lot of room for creating false impressions. The same goes for pictograms and infographics.
- "The semi-attached figure": This means stating one thing as a proof for something else. For example, if an ad says "15% of CEOs drive a Buick; more than any other brand"— what does that prove? The implication is that CEOs are some sort of authorities on cars. This is a common tactic. Remember the old Certs commercials, where the narrator says, "Certs. Now with Retsyn!" Did anyone even know what Retsyn is or why should we care?
- "Post-hoc fallacy": This is incorrectly asserting that there is a direct correlation between two findings. This is particularly nefarious but it is often more difficult to catch than the other tactics. For example, if a study finds that vegetarians have a higher average income than meat-eaters, it would be absurd to conclude that you can raise your income by abstaining from meat. But that is exactly what some ‘researchers’ do.
Huff presents an entire chapter of how to identify spotty statistics, which I will revisit those in a future post. In the meantime, the best advice, as always, is to be skeptical. Caveat emptor!
Author David Lavenda is a high tech marketing and product strategy executive who also does academic research on the effects of information overload on organizations. He is an international scholar for the Society for the History of Technology.
[Image: Flickr user MervC]