I saw a few headlines about new law in Michigan that would exempt most white Medicaid recipients from work requirements, but keep the work requirement for most black people in the same spot. This sounded like a terrible plan, so I went looking for some background and found this article that explains the whole thing. Basically some lawmakers thought that the work requirements didn’t make sense for people who lived in areas of high unemployment, but they decided to calculate employment at the county level. This meant that 8 rural-ish counties had their residents exempted, but Detroit and Flint did not. Those cities have really high unemployment, but they sit in the middle of counties that do not. The complaints here seem valid to me….city dwellers tend not to have things like cars, so the idea that they can reverse commute out to the suburbs may be a stretch. 10 miles in a rural area is really different from 10 miles in the middle of a city (see also: food deserts/access issues/etc). Seems like a bit of a denominator dispute.
I’ve talked before about radicalization of people via YouTube, and this Slate article touched on a related phenomena: Netflix and Amazon documentaries. With the relative ease of putting content up on these platforms, things like 9/11 truther or anti-vaccine documentaries have found a home. It’s not clear what can be done about it unfortunately, but it’s a good thing to pay attention to.
I liked this piece from Data Colada on “the (surprising?) shape of the file drawer“. It starts out with a pretty basic question: if we’re using p<.05 as a test for significance, how many studies does a researcher before he/she gets a significant effect where none should exist? While most people (who are interested in this sort of thing) get the average right (20), what he points out is that most of us do not intuit the median (14) or mode (1) for the same question. His hypothesis is that we’re all thinking about this as a normal distribution, when really it’s geometric. In other words the “number of studies” graph would look like this (figure from the Data Colada post):
And that’s what it would look like if everyone was being honest or only had one hypothesis at a time.
Andrew Gelman does an interesting quick take post on why he thinks the replication crisis is centered around social psychology. In short: lower budget/easier to replicate studies (in comparison to biomedicine), less proprietary data, vaguer hypotheses, and the biggest financial rewards come through TED talks/book tours.
Given my own recent bought with Vitamin D deficiency, I was rather alarmed to read that 80% of African Americans were deficient in Vitamin D. I did some digging and found that apparently the test used to diagnose Vitamin D deficiency is actually not equally valid across all races, and the suspicion is that African Americans in particular are not served well by the current test. Yet another reason to not assume research generalizes outside it’s initial target population.
This Twitter thread covered a “healthy diets create more food waste” study that was getting some headlines. Spoiler alert: it’s because fruits and veggies go bad and people throw them out, whereas they tend to eat all the junk food or meat they buy. In other words, if you’re looking at environmental impact of your food, you should look at food eaten + food wasted, not just food wasted. The fact that you finish the bag of Doritos but don’t eat all your corn on the cob doesn’t mean the Doritos are the winner here.