Hi friends, hope we’re all keeping well.
This week’s mental model is called the Chinese Robber Fallacy, which I have honestly never heard about until just recently when I was looking for new mental models to talk about. And having such a peculiar name, of course I couldn’t resist learning more.
So to better my understanding of this model, I’ve decided to talk about it on this week’s MMM🙃
My (rough) definition:
The Chinese Robber Fallacy is a fallacy, or mistake, that occurs when you’re only focusing on a small subgroup of people, without acknowledging or comparing it to a larger group.
Pretty much a fallacy that perfectly encapsulates ‘stereotyping’, and usually occurs hand in hand with bigotry or racism.
As in, and this person puts it quite nicely,
This informal fallacy is frequently used by racists and bigots, but more generally can be made whenever someone focuses on any single demographic without looking at per-capita statistics.Franklin Veaux, Quora
-Let’s have a look at an example that directly gave the fallacy its name.
Say we have a bigot who’s extremely xenophobic, and heavily dislikes Chinese people. To try justify his hatred, he goes and searches for statistics to ‘prove’ that all Chinese people are criminals. Let’s say he is ‘somewhat successfully, and discovers 1,000 Chinese people were arrested in the nation last year.
“Look at how many Chinese people committed crimes last year in this country alone! A whopping 1,000 were arrested! This proves that the Chinese are all criminals!”
Yes, but have we also looked at how many non-Chinese people committed crimes last year? Or how many Chinese people in this country simply didn’t commit a crime? How are the Chinese more prone to commit a crime than anyone else?
(To refute this fallacy, we simply compare this small group of people to a bigger group of related people)
-Donald Trump has also fallen victim to this fallacy before, when he claimed that the U.S was doing more COVID tests than any other country in the world at the time. That would be true if we were going by pure numbers alone, but what we obviously want is the rate of testing per capita (for each person), which is much more accurate.
Let’s say in 2020 the US was doing 10 times as many COVID tests as Ireland.
That is absolutely nothing to brag about since we’re not taking into account the fact that the US population is 66x larger than Ireland.
Can you see how he was guilty of this error by only focussing on the total number of tests administered, without acknowledging the US has more people than in other countries?
-To give a more exaggerated example, let’s say we have 2 colleges trying to compare student graduation rates. In college A, they claim that a total of 100 of their students made it all the way to graduation.
College B, however, boasts that 500 of their students made it to graduation; five times as many students from college A.
It would be easy to say that the competition is already over, and that since college B clearly has more graduates, they also have a higher graduation rate.
But we’re forgetting to compare how many students actually entered the colleges in the first place.
Let’s say in college B, while 500 students did made it to graduation, another 2,000 didn’t, and dropped out at one point or another. And then say in college A, while yes only 100 students graduated, this was actually half the entire amount of students that entered the college (so, only 100 dropped out).
Thus, the true graduation rates now appear: College A actually has a 50% graduation rate, while college B only has a 20% graduation rate.
Do you see how the Chinese Robber Fallacy can easily be made when interpreting such statistics? It is always important to look at the bigger picture, and look for the ‘larger’ group to compare.
Whoo, that’s another mental model down.Truth be told, I almost forgot to write one for this week, so I tried blitzing this one out today 😅
Hopefully the quality wasn’t too abysmal (though I suppose most of my posts’ quality are🤦🏻♂️)
Stay safe, and I’ll hopefully see you in another post👋🏼
sometimes the percentages matter, but sometimes the raw numbers are relevant as well…
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Of course, always handy to keep both measurement methods in mind
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