Stat-Spotting. Joel Best
a press release, the British Heart Foundation’s director for Scotland was quoted as saying: “We know that regular exposure to second-hand smoke increases the chances of developing heart disease by around 25%. This means that, for every four non-smokers who work in a smoky environment like a pub, one of them will suffer disability and premature death from a heart condition because of second-hand smoke.”5
Well, no, that isn’t what it means–not at all. People often make this blunder when they equate a percentage increase (such as a 25 percent increase in risk of heart disease) with an actual percentage (25 percent will get heart disease). We can make this clear with a simple example (the numbers that I am about to use are made up). Suppose that, for every 100 nonsmokers, 4 have heart disease; that means the risk of having heart disease is 4 per 100. Now let’s say that exposure to secondhand smoke increases a nonsmoker’s risk of heart disease by 25 percent. What’s 25 percent of 4? One. So, among nonsmokers exposed to secondhand smoke, the risk of heart disease is 5 per 100 (that is, the initial risk of 4 plus an additional 1 [25 percent of 4]). The official quoted in the press release misunderstands what it means to speak of an increased risk and thinks that the risk of disease for nonsmokers exposed to secondhand smoke is 25 per 100. To use more formal language, the official is conflating relative and absolute risk.
The error was repeated in a newspaper story that quoted the press release. It is worth noting that at no point did the reporter quoting this official note the mistake (nor did an editor at the paper catch the error).6 Perhaps they understood that the official had mangled the statistic but decided that the quote was accurate. Or–one suspects this may be more likely–perhaps they didn’t notice that anything was wrong. We can’t count on the media to spot and correct every erroneous number.
Translating statistics into more easily understood terms can help us get a feel for what numbers mean, but it may also reveal that those doing the translation don’t understand what they’re saying.
C3Misleading Graphs
The computer revolution has made it vastly easier for journalists not just to create graphs but to produce jazzy, eye-catching displays of data. Sometimes the results are informative (think about the weather maps—pioneered by USA Today—that show different-colored bands of temperature and give a wonderfully clear sense of the nation’s weather pattern).
But a snazzy graph is not necessarily a good graph. A graph is no better than the thinking that went into its design. And even the most familiar blunders—the errors that every guidebook on graph design warns against—are committed by people who really ought to know better.7
| LOOK FORGraphs that are hard to decipherGraphs in which the image doesn’t seem to fit the data |
EXAMPLE: SIZING UP METH CRYSTALS
The graph shown here appeared in a major news magazine.8 It depicts the results of a study of gay men in New York City that divided them into two groups: those who tested positive for HIV, and those who tested negative. The men were asked whether they had ever tried crystal meth. About 38 percent of the HIV-positive men said they had, roughly twice the percentage (18 percent) among HIV-negative men.
Although explaining these findings takes a lot less than a thousand words, Newsweek decided to present them graphically. The graph illustrates findings for each group using blobs–presumably representing meth crystals. But a glance tells us that the blob/crystal for the HIV-positive group is too large; it should be about twice the size of the HIV-negative group’s crystal, but it seems much larger than that.
Graph with figures giving misleading impression.
We can guess what happened. Someone probably figured that the larger crystal needed to be twice as tall and twice as wide as its smaller counterpart. But of course that’s wrong: a figure twice as wide and twice as tall is four–not two–times larger than the original. That’s a familiar error, one that appears in many graphs. And it gets worse: the graph tries to portray the crystals as three dimensional. To the degree that this illusion is successful, the bigger crystal seems twice as wide, twice as tall, and twice as deep–eight times larger.
But what makes this graph really confusing is its use of different-sized fonts to display the findings. The figure “37.8%” is several times larger than “18%.” Adding to the problem is the decision to print the larger figure as three digits plus a decimal point, while its smaller counterpart has only two digits. The result is an image that manages to take a simple, easily understood comparison between two percentages and convey a wildly misleading impression.
We can suspect that the ease with which graphic artists can use computer software to manipulate the sizes of images and fonts contributed to this mangled image. Attractive graphs are preferable to ugly graphs–but only so long as artistic considerations don’t obscure the information the graph is supposed to convey.
C4Careless Calculations
Many statistics are the result of strings of calculations. Numbers—sometimes from different sources—are added, multiplied, or otherwise manipulated until a new result emerges. Often the media report only that final figure, and we have no easy way of retracing the steps that led to it. Yet when statistics seem incredible, when we find ourselves wondering whether things can possibly be that bad, it can be worth trying to figure out how a number was brought into being. Sometimes we can discover that the numbers just don’t add up, that someone almost certainly made a mistake.
| LOOK FORAs with other sorts of blunders, numbers that seem surprisingly high or lowNumbers that seem hard to produce–how could anyone calculate that? |
EXAMPLE: DO UNDERAGE DRINKERS CONSUME 18 PERCENT OF ALCOHOL?
A 2006 study published in a medical journal concluded that underage and problem drinkers accounted for more than a third of the money spent on alcohol in the United States.9 The researchers calculated that underage drinkers (those age 12–20) consume about 18 percent of all alcoholic drinks–more than 20 billion drinks per year. Right away, we notice that that’s a really big number. But does it make sense?
Our benchmarks tell us that each recent age cohort contains about 4 million people (that is, there are about 4 million 12-year-olds, 4 million 13-year-olds, and so on). So we can figure there are about 36 million young people age 12–20. If we divide 36 million into 20 billion, we get more than 550 drinks per person per year. That is, young people would have to average 46 drinks per month. That sure seems like a lot.
Of course, many underage people don’t drink at all. In fact, the researchers calculated that only 47.1 percent were drinkers. That would mean that there are only about 17 million underage drinkers (36 million × .471): in order for them to consume 20 billion drinks per year, those young drinkers would have to average around 1,175 drinks per year–nearly 100 drinks per month, or about one drink every eight hours.
But this figure contradicts the researchers’ own data. Their article claims that underage drinkers consume an average of only 35.2 drinks per month. Let’s see: if we use the researchers’ own figures, we find that 17 million underage drinkers × 35.2 drinks per month equals a total of just under 600 million drinks per month, × 12 months per year = equals 7.2 billion drinks by underage drinkers per year–not 20 billion. Somehow, somewhere, someone made a simple arithmetic error, one that nearly tripled the estimate of what underage drinkers consume. According to the researchers, Americans consume 111 billion drinks per year. If youths actually drink 7.2 billion of those, that would mean that underage drinkers account for about 6.5 percent–not 18 percent–of all the alcohol consumed.
The fact that we can’t make the researchers’ own figures add up to 20 billion drinks is not the end of the story.10 One could go