Bitterness and Psychopathy

I’m having some insomnia problems at the moment, so it was about 4am today when I turned on my coffee maker and sat down to do some internet perusing. I was just taking my first sip, when I stumbled upon this article titled “People Who Take Their Coffee Black Have Psychopathic Tendencies“.

Oh. Huh.

As a fairly dedicated black coffee drinker, I had to take a look.  The article references a study here that tested the hypothesis that an affinity for bitter flavors might be associated with the “Dark Tetrad” traits: Machiavellianism, psychopathy, every day sadism and narcissim.

I read through the study1, and I thought it was a good time to talk about effect sizes. First, lets cover a few basics:

  1. This was a study done through Mechanical Turk
  2. People took personality tests and rated how much they liked different foods, the researchers ran some regressions and reported the correlations  for these results
  3. They did some other interesting stuff to make sure people really liked the bitter versions of the foods they were rating and to make sure their results were valid

Alright, so what did they find? Well, there was a correlation between preference for bitter tastes and some of the “Dark Tetrad” scores, especially everday sadism2. The researchers pretty much did what they wanted to do, and they found statistically significant correlations.

So what’s my issue?

My issue is we need to talk about effect sizes, especially as this research gets repeated. The correlation between mean bitter taste preference and the “Dark Tetrad” scores over the two studies ranged from .14 to .20.  Now that’s a significant finding in terms of the hypothesis, but if you’re trying to figure out if a black coffee drinker you love might be a psychopath3? Not so useful.

See, an r of .14 translates in to an R2 of about .02. Put in stats terms, that means that 2% of the variation in psychopathy score can be explained by4 variation in the preference for bitter foods or beverages. The other 98% is based on things outside the scope of this study. For r = .2, that goes up to 4% explained, 96% unexplained.

Additionally, it should be made clear that no one bitter taste was associated with these traits, only the overall score on ALL bitter foods was.  So if you like coffee black, but have an issue with tonic water or celery, you’re fine.

The researchers didn’t include the full list of foods, but I was surprised to note that they included beer as one of the bitter options. Especially when looking at antisocial tendencies, it seems potentially confounding to include a highly mood altering beverage alongside foods like grapefruit. I’d be interested in seeing the numbers rerun with beer excluded.


1. And no, I didn’t add cream to my coffee. Fear me.
2. It’s worth noting that the mean score for this trait was lower than any other trait however…1.77 out of 5. It’s plausible that only the bottom of the range was tested.
3. Hi honey!
4. In the mathematical sense that is, this does not prove causation by itself

Political Arithmetic – Voter ID laws

Update: Link fixed

Last week I put up a post slamming an infographic on fair market rent between states.  I was interested in the AVIs response, which end with “These are advocacy numbers.  Not the same as actual reality.”

Advocacy and other political skewings of data are one of those things that shouldn’t bother me, but do.  
I read headlines, knowing that I’m going to be driven nuts but the presumptions and projections, and yet I read things anyway.  It’s a bad habit.
All that being said, I truly enjoyed Nate Silver’s examination of the real effect voter ID laws might have on voter turnout in various states. 
He attempts to cut through all the partisan hoopla and to do a one person point-counterpoint.  An example:

But some implied that Democratic-leaning voting groups, especially African-Americans and Hispanics, were more likely to be affected. Others found that educational attainment was the key variable in predicting whom these laws might disenfranchise, with race being of secondary importance. If that’s true, some white voters without college degrees could also be affected, and they tend to vote Republican.

He also makes a fascinating point about the cult of statistical significance:

Statistical significance, however, is a funny concept. It has mostly to do with the volume of data that you have, and the sampling error that this introduces. Effects that may be of little practical significance can be statistically significant if you have tons and tons of data. Conversely, findings that have some substantive, real-world impact may not be deemed statistically significant, if the data is sparse or noisy.

On the whole, he concludes it will swing in the Republican direction for this election, but reminds everyone:

One last thing to consider: although I do think these laws will have some detrimental effect on Democratic turnout, it is unlikely to be as large as some Democrats fear or as some news media reports imply — and they can also serve as a rallying point for the party bases. So although the direct effects of these laws are likely negative for Democrats, it wouldn’t take that much in terms of increased base voter engagement — and increased voter conscientiousness about their registration status — to mitigate them. 

The whole article is long but a great read about how to assess policy changes if you’re trying to get to the truth, rather than just prove a political point.