On Average: 3 Examples

A few years ago now, I put up a post called “5 Ways that Average Might Be Lying to You”. I was thinking about that post this week, as I happened to come across 3 different examples of confusing averages.

First up was this paper called “Political Advertising and Election Results” which found that (on average) political advertising didn’t impact voter turnout. However, this is only partially true. While the overall number of voters didn’t change, it appeared the advertising increased the number of Democrat voters turning out while decreasing Republican turnout. The study was done in Illinois so it’s not clear if this would generalize to other states.

The second paper was “How does food addiction influence dietary intake profile?“, which took a look at self reported eating habits and self reported score on the Yale Food Addiction Scale. Despite the fact that people with higher food addiction scores tend to be heavier, they don’t necessarily report much higher food intake than those without.  The literature here is actually kind of conflicted, which suggests that people with food addiction may have more erratic eating patterns than those without and thus may be harder to study with 24 hour dietary recall surveys. Something to keep in mind for nutrition researchers.

Finally, an article sent to me by my brother called “There is no Campus Free Speech Crisis” takes aim at the idea that we have a free speech problem on college campuses. It was written in response to an article on Heterodox Academy that claimed there was a problem. One of the graphs involved really caught my eye. When discussing what percentage of the youngest generation supported laws that banned certain types of speech, Sachs presented this graph:

From what I can tell, that’s an average score based on all the different groups they inquired about. Now here’s the same data presented by the Heterodox Academy group:

Same data, two different pictures. I would have been most interested to hear what percentage of the age ranges supported laws against NO groups. People who support laws against saying bad things about the military may not be the same people who support laws against immigrants, so I’d be interested to see how these groups overlapped (or not).

Additionally, the entire free speech on campus debate has been started by outliers that are (depending on who you side with) either indicative of a growing trend or isolated events that don’t indicate anything. Unfortunately averages give very little insight in to that sort of question.

Maternal Mortality and Miscounts

I’m a bit late to the party on this one, but a few weeks ago there was a bit of a kerfluffle around a comment from a Congressman from Minnesota’s comments about maternal mortality in states like Texas and Missouri:

Now I had heard about the high maternal mortality rate in Texas, but it wasn’t until I read this National Review article about the controversial Tweet that I discovered that the numbers I’d heard reported may not be entirely accurate.

While it’s true that Texas had a very high rate of maternal mortality reported a few years ago, the article points to an analysis done after the initial spike was seen. A group of Texas public health researchers went back and recounted the maternal deaths within the state, this time trying a different counting method. Instead of relying on deaths that were coded as occurring during pregnancy or shortly afterward, they decided to actually look at the records and verify  that the women had been pregnant. In half the cases, they found that no medical records could be found to corroborate that the woman was pregnant at the time of death. This knocked the maternal mortality rate down from 38.4 per 100,000 to 14.6 per 100,000.  Yikes.

The problem appeared to be the way the death certificate itself was set up. The “pregnant vs not-pregnant” status was selected via dropdown menu. The researcher suspected that the 70 or so miscoded deaths were due to people accidentally clicking on the wrong option. They suggested replacing a dropdown with a radio button. To make sure this error wasn’t being made in both directions, they did actually go back and look at fetal death certificates and other death certificates for women of child bearing age to make sure that some weren’t incorrectly classified in the other direction. Unsurprisingly, it appears that when people want to classify a death as “occurring during pregnancy” they didn’t tend to make a mistake.

The researchers pointed out that such a dramatic change in rate suggested that every state should probably go back and recheck their numbers, or at least assess how easy it would be to miscode something. Sounds reasonable to me.

This whole situation reminded me of a class I attended a few years back that was sponsored by the hospital network I work for. Twice a year they invite teams to apply with an idea for an improvement project, and they give resources and sponsorship to about 13 groups during each session. During the first meeting, they told us our assignment was to go gather data about our problem, but they gave us an interesting warning. Apparently every session at least one group gathers data and discovers the burning problem that drove them to apply isn’t really a problem. This seems crazy, but it’s normally for reasons like what happened in Texas. In my class, it happened to a pediatrics group who was trying to investigate why they had such low vaccination rates in one of their practices. While the other 11 clinics were at >95%, they struggled to stay above 85%. Awareness campaigns among their patients hadn’t helped.

When they went back and pulled the data, they discovered the problem. Two or three of their employees didn’t know that when a patient left the practice, you were supposed to click a button that would take them off the official “patients in this practice” list. Instead, they were just writing a comments that said “patient left the practice”. When they went back and corrected this, they found out their vaccination rates were just fine.

I don’t know how widespread this is, but based on that classroom anecdote and general experience, I wouldn’t be surprised to find out 5-10% of public health data we see has some serious flaws. Unfortunately we probably only figure this out when it gets bad enough to pay attention to, like in the Texas case. Things to keep in mind.

Bilingualism in Units of Measure

Well, I’m back from Germany and everything went quite well, except for one little incident with a spontaneous bloody nose brought on by the descent in to the Atlanta airport. Thankfully there’s a bathroom before you actually have to go through customs in Atlanta (there was not in Stuttgart), because I’m pretty sure the border patrol folks would have been less than impressed at my attempts to clean myself up with my leftover bottled water and that weird mesh they cover the complimentary pillow with. Good times.

It was a fun trip overall, and my lack of German didn’t end up making a difference. The town we were in was a college town, so nearly everyone spoke English as a second language. It was a little interesting though, as it was clear very few people we talked to were used to conversing with native English speakers (we saw quite a few people conversing in English where it was clear they were both ESL with different primary languages), which led to some fascinatingly idiosyncratic translation issues. For example, one of the people we spent the most time with clearly only knew the pronoun “he” and applied it to everything. The sign above the coat rack in our hotel informed us “We are not responsible for your wardrobe”, which didn’t quite come off as I believe they intended it. Not judging of course, since it’s all better than my forays in to other languages, but I actually love seeing where the unusual phrasing comes up.

Anyway, while thinking about various translation issues, I started thinking a little bit about units of measure. There were a few times over the course of the week where distances or volumes came up, and I was interested to see that I have minimal problems translating kilometers to miles/pounds to kilograms/liters to quarts or vice versa. Part of this is just general quick mental math, but I did realize that I’m actually pretty comfortable in thinking in either the metric system or the US/imperial system. My engineering degree and lab work both used a lot of metric system units, and being a runner keeps you familiar with 5k and 10k distances, which make all the distance translations pretty straightforward.

The only unit I have real trouble with is temperature. I simply cannot think in Celsius. Every time I see a temperature in Celsius I have to spend quite a bit of time calculating before I get to the right ballpark. I’m not sure why this is, though I suspect it’s something about the simultaneous change in the magnitude of a degree and the reference numbers. Somehow trying to doing both at once throws me off.

I’m curious how many people are actually comfortable in both sets of units. I’m guessing there’s a strong influence of profession here.

On a related note, here’s the history of the US relationship to the metric system as told by NIST.

On an unrelated note, here’s a map of Europe and what each region calls Germany:

Apparently this is directly correlated with which occupants of Germany invaded which country first, though I can’t confirm that.

Public Interest vs Public Research

Slate Star Codex has up an interesting post up about a survey he conducted on sexual harassment by industry. While he admits there are many limitations to his survey (it was given to his readers), the data is still interesting and worth looking at.  He has a decent overview of why some surveys yield low numbers (normally by asking “have you been harassed at work?”) and some high (by asking specific questions like “have you been groped at work?”), that actually serves as an interesting case study for how to word survey questions.  Words like “harassed” tend carry emotional weight for people, so including them in surveys can be a mixed bag.

Anyway, data questions aside, I was a little fascinated by something he said at the end of his post that caught my interest “This may be the single topic where the extent of public interest is most disproportionate to the minimal amount of good research done.”

His complaint is that for all we hear about certain industries being rife with sexism and harassment (and those two terms frequently being conflated), he couldn’t find much real research on which industries were truly the worst.

I think that’s a really interesting point, but it got me wondering what other public interest questions don’t have much research behind them. My first thought was gun research. While not technically banned, back in 1996 an amendment went through that cut the CDC budget by the amount they had previously been spending on firearm research and included a rule that federal dollars couldn’t be spent “to advocate or promote gun control”. This comes up every time there’s a shooting like Parkland, and people are looking to overturn it. While I’ve mostly heard gun control advocates talk about this, it’s interesting to note that not all the pre-Dickey amendment research cast guns in a bad light. Reason magazine recently put up an article highlighting how little we know about how often guns are used for self-defense purposes, and how the CDCs last numbers put it at much higher than I would have thought (1.5% of Americans per year).

I’m curious if people can think of other topics like this.

Germans and Bone Marrow Donation

I’m headed to Germany for the week to visit a bone marrow collection center there. Most people don’t know this, but 25-33% of all the world’s unrelated volunteer bone marrow donors are managed by the German national registry.

Even though bone marrow transplants were pioneered in the US,  Germany and the Netherlands that took a lead on building registries where people could look for donors if someone didn’t have a suitable sibling donor. As they started to build their own registries, they also got backing from their governments to recruit people to them as well. German participation in the bone marrow registry is about double the US (almost 10% vs 5%).

Anyway, I’ll be giving two talks (thankfully with a colleague) that are supposed to go 3 hours. Since I only know about 10 German words, this could get interesting. Fingers crossed for me!

The Mandela Effect: Group False Memory

I’ve talked a lot on this blog about the problems with memory, but I realized recently I’d never done a post on the topic of group false memory, particularly the Mandela Effect. If you’ve never heard of that, it’s a term that was invented/popularized by this website discussing the phenomena of “…apparently real, alternate memories of a history that doesn’t match the documented history in this reality.” More specifically, these are very vivid memories about pop culture or world events, held by multiple people with no association to each other, that were not true.

Now unlike many things I write about here, the interesting part about these is that they are not motivated by anything in particular. No one gains any ego/political/social points by believing them. The phenomena was actually named when the woman who started site realized that she had erroneously believed that Nelson Mandela died in prison rather than in 2013. She thought she was confused, until she later heard a near stranger at a conference mention that they had believed the same thing.  Eerily, this was not a “I thought I heard that”, they both had full memories of news and seeing the funeral.

Some other common examples:

More here.

I specifically listed the three above because those are all memories I have, and I was surprised to find out none of them were real (well, I figured out my Billy Graham memory was wrong in February, but I was surprised to find out I wasn’t the only one who clearly “remembered” this). The chartreuse one particularly surprised me because I remember googling it when “Frozen” came out and Olaf mentions it. Maybe it’s because he says “crimson” right before, but I’m not clear how I looked that up and still remembered it incorrectly.

Anyway, there’s not a great theory for why this happens,  other than that alternate universes occasionally open up and drop alternate realities on us. Kidding. Sorta.

Seriously though, the best suggestion is that around some events there’s enough subtle cues that large numbers of people get them mixed up. Like the for the Berestain Bears, most of us have met people with a last name ending in -stein, so at some point we think it looks more correct. Combine that with the loopy handwriting from the books and the southern twang influenced pronunciation in the TV show theme song, and you’ve got a mass memory of a name that never existed.

The other idea is that events or people that are in the news at the same time might get conflated. For example this article points out that when asked to identify former US presidents, many people will say Alexander Hamilton was while missing actual former presidents. However,  we know that’s because most people learn about him at the same time they are learning about the founding fathers, so they associate him with that. It’s possible a similar thing happens with events. Did Nelson Mandela make headlines for something else around the same time someone else’s funeral was going on? Maybe! That would be really hard to track back, but it’s plausible. If even 1% of people seeing those headlines conflated them at some point later, that could seem pretty freaky…..especially now that they can gather on the internet.

You know, it’s that or we’re all on the hologram deck.

Weird Weather on Patriots’ Day

Well folks, tomorrow is Patriots’ Day/Marathon Monday here in Massachusetts, which means the kind of lousy weather we’re having is going to affect the Boston Marathon runners. That’s a pity, but I’m pleased that the weather was at least okay yesterday, as my son went to his first major league game with his dad and grandfather. Since he’s being raised in a mixed household (I’m a Red Sox fan as is his grandfather, his father is an Orioles fan), he went with an Orioles shirt/Red Sox hat outfit that apparently was quite a hit with the crowd. My husband was good natured about it, until he got stopped by the MASN camera crew who were wandering around trying to find a few Orioles fans in Fenway. He refused to risk being on an Orioles broadcast with a child in a Red Sox hat, so he pulled his spare Orioles hat out of his coat pocket and our kiddo got his TV debut. We haven’t been able to find the clip, but we’re still looking.

Anyway, with the weather going downhill today, my Dad and I started musing about the worst marathon weather we could remember. I mentioned 2012 when it got so hot that they proactively offered to defer entries, and my Dad mentioned that in 1976 it hit 96 degrees. This led me to a page on the Boston Athletic Association’s website about all the weird weather they’ve gone through over the years.

A few highlights:

  • 5 different years saw snow fall on the marathon
  • 1939 saw a partial eclipse
  • 3 years have seen driving rain
  • 1927 saw heat (84 degrees) and a newly paved road that melted under their feet
  • At least 4 marathons have been run in 90+ degree weather

I’m hoping that our weird weather gives hometown girl Shalene Flanagan an edge, as I’m cheering hard for her. The last time someone from Massachusetts won the Boston Marathon was Alberto Salazar in 1982, I think we’re due.

One interesting tidbit I never knew about Patriots’ Day: in Maine, it’s legally “Patriot’s Day”, which makes me incredibly happy. That is going to be my go to excuse if I screw it up at any point going forward.