An example of a significant misinterpretation of survey data in a recent book provides a reminder of about reading “facts”:
There are a few major lessons here. The first is that books are not subject to peer review, and in the typical case not even subject to fact-checking by the publishers — often they put responsibility for fact-checking on the authors, who may vary in how thoroughly they conduct such fact-checks and in whether they have the expertise to notice errors in interpreting studies, like Wolf’s or Dolan’s.
The second, Kimbrough told me, is that in many respects we got lucky in the Dolan case. Dolan was using publicly available data, which meant that when Kimbrough doubted his claims, he could look up the original data himself and check Dolan’s work. “It’s good this work was done using public data,” Kimbrough told me, “so I’m able to go pull the data and look into it and see, ‘Oh, this is clearly wrong.’”…
Book-publishing culture similarly needs to change to address that first problem. Books often go to print with less fact-checking than an average Vox article, and at hundreds of pages long, that almost always means several errors. The recent high-profile cases where these errors have been serious, embarrassing, and highly public might create enough pressure to finally change that.
In the meantime, don’t trust shocking claims with a single source, even if they’re from a well-regarded expert. It’s all too easy to misread a study, and all too easy for those errors to make it all the way to print.
These are good steps, particularly the last paragraph above: shocking or even surprising statistics are worth checking against the data or against other sources to verify. After all, it is not that hard for a mutant statistic to spread.
Unfortunately, correctly interpreting data continues to get pushed down the chain to readers and consumers. When I read articles or books in 2019, I need to be fairly skeptical of what I am reading. This is hard to do with (1) the glut of information we all face (so many sources!) and (2) needing to know how to be skeptical of information. This is why it is easy to fall into filtering sources of information into camps of sources we trust versus ones we do not. At the same time, knowing how statistics and data works goes a long way in questioning information. In the main example in the story above, the interpretation issue came down to how the survey questions were asked. An average consumer of the book may have little idea to question the survey data collection process, let alone the veracity of the claim. It took an academic who works with the same dataset to question the interpretation.
To do this individual fact-checking better (and to do it better at a structural level before books are published), we need to combat innumeracy. Readers need to be able to understand data: how it is collected, how it is interpreted, and how it ends up in print or in the public arena. This usually does not require a deep knowledge of particular methods but it does require some familiarity with how data becomes data. Similarly, being cynical about all data and statistics is not the answer; readers need to know when data is good enough.