(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
I got two of these in the same day!
1. A colleague emails me that a colleague emailed him regarding a study on women in the workplace. The headline conclusion is: “Corporate America is not on a path to gender equality.” My colleague’s colleague writes:
This coincides with my prior beliefs, but for exactly that reason I thought it important to dig into the evidence and see what the latest numbers show. . . .
He then went into all sorts of problems he had with the report, and then summarized:
I am just curious why the report chooses to focus on “we aren’t there yet, and therefore are not on the path” when in fact it looks from the data like we are *exactly* on the path.
I *do* have a lot of sympathy for the report. I think the recommendations at the end are sensible, and that many of the statements about forms of cultural bias and inequality in the home, etc., are correct. But these are based on other data. This study claims to present novel conclusions that are not supported by the data presented.
My colleague sent me the above with the note that it might be of interest.
I replied: The last thing I want to do, as a man, is to say that progress in gender equality is just fine—so I think I’ll stay out of this one!
2. Someone else points me to a news article entitled, “Height May Be Linked to Increased Cancer Risk, Study Contends,” and asks, “What do we do with something so absurd?”
I replied: Too difficult for me to understand . . . I don’t want to touch this one!
P.S. The real selection bias, though, comes when I don’t even tell you I’m not writing about a topic!
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