Michael W. Sances

I haven’t been reading the papers in a few months, so am short on material.

I still Rick Hasen’s Election Law Blog, however, which is always chalk full of interesting policy and causal questions. In the wake of the 2012 election, and the president’s apparently off-handed comment about long lines at the polling places, there is apparently some very tiny rumblings about nationalizing election administration in the U.S. For example, Hasen himself argued in the NYT’s “Room for Debate” forum that elections should be nationalized. In response, Doug Chapin gave some reasons why not, citing both the (apparently normative) virtues of federalism and the perceived gridlock and incompetence of the feds right now.

Slightly more interesting is why or why not reforming election administration is “a thing” or not, in Chapin’s language. That is, why is it so remote from the policy agenda? It’s easy to say that it’s a non-starter and that state and local governments really don’t want to centralize, but that seems like begging the question. Indeed it’s even more mysterious when (to crib another item from Hasen’s blog) a poll finds 88% of Americans supporting a uniform system!

Perhaps it’s not hard to dig up other items where the public seems to speak with such a loud voice, and in contrast to what policy is or what elites think it should be. But the fact is that usually we laud correlations between mass opinion and public policy as a good thing, and decry low or negative correlations as a bad thing, for democracy. So it would seem inconsistent to just write this off as an anomaly.

Via Election Law Blog, this New York Times op-ed by two ex-senators gives an unconventional justification for disclosure: to help incumbents ward off anonymous attacks:

Without the transparency offered by the Disclose Act of 2012, we fear long-term consequences that will hurt our democracy profoundly. We’re already seeing too many of our former colleagues leaving public office because the partisanship has become stifling and toxic. If campaigning for office continues to be so heavily affected by anonymous out-of-district influences running negative advertising, we fear even more incumbents will decline to run and many of our most capable potential leaders will shy away from elective office.

I suppose it makes sense to argue this if you are speaking to senators’ self
Interest, but it might not look great to voters.

Interesting New York Times article. Great metaphor for science.

With a caveat that this is all filtered through an NPR show, I found this interview on the Higgs Boson `quasi-discovery’ relevant to current discussions of what’s supposedly wrong with social science.

I transcribed parts of the interview, which took place between WAMU host Celeste Headlee and Scientific American associate editor John Matson.

Headlee: Scientists needed to find out, whether or not the standard theory held water: why does matter hold mass? And they may have gotten their proof. I say may because we don’t know exactly what they’ve found yet, other than that it’s a new subatomic particle. Remember last year scientists at CERN claimed they had discovered particles that were faster than the speed of light. You remember that? Then they had to retract that claim. So the burden of proof when you discover something new…the standard is pretty high. The folks at today’s announcement needed to be certain if they had something…

Tell me about their certainty here. What standard did they use?

Matson: Sure. So the standard, uh sort of physics measure is sigma, or standard deviations. So they say if they have 3 sigma evidence that’s good for evidence of a new factor a new particle. 5 sigma is a discovery. And 5 sigma relates to a 1 in 3 and a 1/2 million chance that it’s just a statistical fluke, you know you’re just seeing some noise that looks like something real. And in this case they made it to that, they made it to 5 sigma, so this is certainly a very strong effect that they’re seeing.

Headlee: [...] So it’s like a 1 in 3.5 million chance it’s _not_ a new particle, right?

Matson: Well assuming that they’ve gotten everything correct, and that is where the faster than light particle finding comes in. That was a very high sigma effect, but wrong in other ways. So there’s always a chance there could be something funny going on, but in this case with this particle that looks like it could be the Higgs, that’s probably not going to happen, some mundane explanation, because there are two different experiments that are seeing what looks like the same thing, it’s similar to what’s been predicted for decades, so everything sort of rings true here, whereas with the faster than light neutrino thing last year, that sort of came out of no where, there was really no other experiment that supported that, and it went against decades and decades of scientific findings that said this shouldn’t be possible.

Here’s what caught my attention in particular.

• Arbitrary standards of significance. In social science we have p-values, and apparently in physics they have sigmas, but they both contain the same information: how likely is it that what we’ve found is a result of random chance? Matson states that 5 sigma is the standard for a “discovery” in physics, where a discovery is not quite going the whole way. And he says that the researchers just barely made it to 5. But in fact, a researcher is quoted in the piece as saying (apparently at a press conference), “We conclude by saying that we have observed a new boson with a mass of 125.3, plus or minus .6 GV, at 4.9 standard deviations.”
• Significance is only meaningful when combined with assumptions. A precisely estimated point estimate is meaningless if it is biased. As Matson is quick to point out, the significance comes from a null hypothesis that is based on model assumptions: it’s a 1 in 3.5 million chance they are wrong, “assuming they’ve gotten everything correct.”
• Fragility of results. I like how the story keeps circling back to the “faster than light neutrino” finding and retraction from last year. It’s instructive how they use it as a baseline: we had a huge sigma there, but it was later retracted, so how do we avoid being fooled again? The two pieces of evidence they give are (1) replication–this finding comes from not one but two experiments, and (2) theory–the previous finding contradicted decades of other findings, but this one is consistent with decades of theory. While it makes sense to believe things that are consistent with other pieces of evidence–whether another experiment, other findings, or other theory–you can’t help but worry that this type of thinking causes us to reject useful information as well.
• Theory testing. The researchers are motivated by the desire to test (implications of) theories. Some say that social science shouldn’t bother with this, but instead focus on “thinking deeply about what prompts human beings to behave the way they do.” Substitute “particles” for “human beings”, and this sort of advice would have prevented such an announcement. I also see an affinity with the CERN director quoted in the story as saying “To know that our maths, our equations, all our Greek symbols tell us some deep truth about everything and what everything is made of, and to have that verified with the discovery of the Higgs – that is one of the great, great moments in science.”

Count me among those who didn’t know realize there was a debate about this, but apparently people have been arguing about whether Medicaid does any good for people. As described in a recent New York Times article, the state of Oregon held a lottery where they randomly assigned Medicaid coverage to 10,000 out of the 90,000 who applied. See also this post at the Monkey Cage.

I like this description of the design from the New York Times:

By assigning coverage randomly, Oregon gave researchers more confidence that they had teased out the true effects of insurance, and had not been fooled by other differences between the insured and the uninsured.

The Times apparently decided the study was not good enough to stand on its own, and decided to interview “17 insured and uninsured participants.” At least we know the treatment was random there, though!