Snakes, and other slithering beasts

I have these snakes where I live, quite a few species, but the dangerous ones where I live are the ones with rattles. Specifically, Crotalus oreganus, but that’s not important.

They bite things. That’s what they do. Like all pit vipers, their skulls are specifically designed to inject venom into other animals. One of them bit my dog once. Others have tried to bite me. This typically ends with a decapitated rattlesnake and another tail in my collection.

I’m confident that there are people who would blame me for all of that. The vipers are in their natural habitat, or maybe they wouldn’t bite if we didn’t agitate them. But we are here, and the conflict will continue no matter what people kvetch about on the sidelines. Even if I simply avoided them, my dog is less safe because they’re around.

There are certain groups of people who, like these snakes, seem to have a natural inclination towards messing up other peoples’ days. We can blather on about this being a self defense mechanism if we wish (more accurate for the snakes than the people, but it really doesn’t matter). We can blame the victims for having colonized or enslaved them in the past, or try to pretend that if we didn’t arrest them so much, they wouldn’t commit so much crime.

But I think it’s more productive to point out that the people who make excuses for Africans and Muslims are often the same people who don’t want me to protect my family from literal pit vipers.

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Cladistics of Human Peoples

To define different human races, people typically use genetic distances and the variation in phenotype between groups.

However, cladistics have become popular among evolutionary biologists and for good reason. For the uninitiated, cladistics are quite simple; groups are related insofar as they have a common ancestor, to the degree of how recent that ancestor lived. I’ll quote myself from the comments section of a Pumpkin Person post on the Big Three racial groups:

To define human races you could use cladistics, genetics, or phenotype.

From a standpoint of cladistics, Australoids, Bushmen, Caucasoids, and Mongoloids are all monophyletic groups with a set of common ancestors that are exclusive to their own kind. The same is probably true for pygmies and Hadza. The remaining African Negroids are a catch-all for everybody else: they have no common ancestors shared with one another that are not shared with Europeans, Asians etc. The only reasons that Congoid African groups resemble one another more than they resemble Europeans or Asians is that they had similar selective pressures and that they interbred.

I’m leaving admixture out of this. Australian Aboriginals for example are share some genes with Europeans, but the Aboriginal racial group has a founding population separate from Europeans. Just because two streams are reunited, doesn’t mean they didn’t split.

Next I compare the cladistic model of categorizing human races with the genetic distance model:

From a standpoint of genetics (most of which may be neutral genome as you suggest) the biggest autosomal genetic distances are between Africans and non-Africans, Caucasians and Asians, and between various African groups (http://www.pnas.org/content/108/13/5154/T1.expansion.html) such that pygmies, Hadza, Bushmen etc are roughly as genetically distinct from one another and from Congoids as Europeans are from Asians. So that lines up with the cladistics. These data would be clearer if someone cross referenced them with admixture analyses: the genetic difference between San and Bantu peoples for example would be even larger if you accounted for the fact that there has been admixture. That said, homozygosity can inflate these distances.

The reason that these two line up is that genetic distances are used to determine, say, whether and when the Bushmen split from other Africans. They’re two different models of the same reality, and sometimes the same data.

This makes it all sound useless: just look at the genetic data, you may insist.

But it adds perspective. Genetics tell us that the Micronesians and Africans are very different, but judging by phenotype you would consider them similar. Cladistic models built with genetic data tell us that Micronesians share a set of ancestors with all non-Africans that they do not share with Africans; they may look similar and act similar, but they’re on a different branch of the family tree.

The question ultimately comes down to whether you’re more concerned with phenotypes or with evolutionary relationships.

Yes, Polls Underestimate Trump

I’ve heard some claims from fellow Trump supporters that our guy’s support is underrated by the polls. This is a likely possibility and not even necessarily the result of bias, because all polls sample the people who the pollsters believe are most likely to vote; if a candidate comes in and pulls new voters with them, they’ll be underrated. I cross referenced Trump’s support in the Republican primaries this year with the polls to see whether they really did underestimate his support.

Scroll down to the first sentence with big letters if you don’t care to read about my methods.

To look into it I did a statistical meta-analysis of RealClearPolitics Republican Presidential primary polls at the state by state level for the 2016 Republican primaries. My null hypothesis was that the polls were equal to or greater than the actual Trump support, and my alternative hypothesis was that Trump support was significantly greater than the polling averages predicted.

The analysis covered 94 polls in 25 states. The rest either had no data, small sample sizes, were outliers, or were months old at the time of the primary election in their respective state. Caucus states were categorically excluded.

I found that the polls underestimate Trump support by 3.2% on average.

Mind you, this is even when they do everything right; recent polls with large sample sizes, averaged with other such polls, should give a pretty accurate result. But they generally fall below the mark.

The only important caveat is that my p value was 0.26. This means that there is, in theory, a 26% chance that the effect I observed is actually due to chance and not a reflection of flawed polling methods.

In practice, I’m willing to bet that the odds of this being a coincidence are less than that. Importantly, my standard deviation for predicted Trump support was massively, artificially inflated by the fact that I went state by state instead of looking at national polls, since primaries are done on a state by state basis. The fact that different states have different opinions about The Donald means that my standard deviation was higher than it would be for Trump support in general, and that means that my p value will also be higher.

Furthermore, there are other instances of this sort of thing happening. A similar effect was observed in Great Britain earlier this summer, with Brexit pulling ahead despite the polls placing them behind. Notably, Farage attributed this effect to a failure to sample new voters. If that effect explained Trump’s support being slightly greater than anticipated, then we would expect an uptick in voter turnout as these new voters rush in to make their mark. Trump got the most votes of any candidate in a Republican primary election in history, and by a wide margin at 30% more than the runner up.

A similar profile characterized Obama’s run in 2008: he was an “outsider” candidate (according to his marketing, anyway), it was assumed that he couldn’t win, but a bunch of people came out of the woodwork and he won with more votes than anyone before him.

Ryan Faulk found that Trump overperformed the primary polls by 3.3% when he did a similar analysis. He also discusses the possibility that the pollsters aren’t reaching Trump supporters proportionally.

The other explanation for this phenomenon is the Shy Trump hypothesis, for which the Morning Consult found some evidence; according to their findings, college educated Republicans are much more likely to admit to supporting Trump over the internet than over the phone.

Regardless of why it happens, however, the takeaway is that there is at least a 74% chance that Trump will do better in the general election than predicted. That chance is higher when you consider my mutant standard deviation.

For my next trick, I’ll see whether polls affiliated with or done by organizations implicated in the DNC leaks peg Clinton support significantly higher than other polls do. Stay tuned.