What would be some fact that, while true, could be told in a context or way that is misinfomating or make the other person draw incorrect conclusions?
Wearing your seatbelt increases your chances of dying from cancer.
People use to say that you cant lie with statistics, but is a common practice to use statistics to lie.
We can take the infamous 41% suicide rate for trans people. Transphobes throw that out like a killing move implying that trans people are inherently unhappy and being trans is a mental illness (which is not true).
The reality is that the suicide rate is so high because of transphobia, kids getting thrown out of home, homelessness, unable to find a job, staying at the closet to avoid social consecuences, etc.
Trans people who live in more open and accepting environments are way less likely to be depressed and commit suicide. In progresive areas where trans people are more accepted the suicide rate is nowhere near 41%.
Dihydrogen Monoxide, commonly used in laundry detergent and other cleaning supplies, is also present in Subway sandwiches
As ice cream sales in the United States increase, so do deaths in in developed parts of Africa.
I use this fact to explain to students how true information can be used to mislead people into drawing wild, deranged conclusions.
The commonality in these events is the rise in temperature during the summer. But if you leave that out, there’s an absurd argument to be made about how purchasing ice cream is inherently evil.
I don’t think it’s an amazing example of what OP is talking about, but as an example, I like how simple and easy to follow it is. Great for junior high level kids.
I’ve never lost a professional MMA match
I don’t know if this counts, since it’s only a “true fact” if you are fine with carefully chosen words and the omission of crucial information…
But the 13-50 stat is dangerously misleading.
You know,
Black people make up 13% of the population, but 50% of the violent crime.
Black people in America do, in fact, make up 50% of the murder arrests according to FBI crime statistics
That much is true.
But certain people tend to use this fact to assert that police officers are far more likely to be killed by black people than by white people. Therefore, the stats that show them brutalizing black people at a higher rate – since they fall short of that 50% number – are evidence that they hold back around black people to avoid appearing racist.
The users of this stat heavily imply black people are more violent and murder-prone, and hence a greater threat. The argument also carries with it an implied benefit to eugenics or a return to slavery (to anyone paying attention.)
But no one using this stat ever explores potential causes for the arrest rate disparity, instead letting their viewers assume it comes from “black culture” (if they are closeted racists) or “bad genes” (if they are open racists).
There’s no attention paid to the fact that black people make up over half of overturned wrongful convictions
There’s no attention paid to the stats further down in that same FBI crime stats table that make it clear that black people make up 25% of the nation’s drug arrests, despite making up close to 13% of the US’s total drug users. (Their population’s rate of drug use is within a margin of error of white people’s rate of drug use). It should be strange that a small portion of the perpetrators of drug crimes make up such an outsized portion of the total drug arrests in this country. But the disparity doesn’t even get a mention.
There’s no attention paid to the fact that more than half of US murders go unsolved, meaning even assuming impartial sentencing and prosecution, we would only know black people committed 50% OF 50% of the murders – 25%. And in a country where 98% of the land is owned by white people and the public defender system is in shambles? Which demographic do you think would be able to afford the best defense, avoiding conviction even when guilty, and ending up overrepresented in the “unsolved murder” category? If only 50% of murders end in a conviction, that means every murderer who walks into a courtroom has a solid chance at getting away with it. Even more solid if the murderer belongs to the richest race. The murder arrest rate by race winds up just being a measure of which demographics can afford the best lawyers, rather than any proportional representation of each demographic’s tendencies.
They mention none of that. The people hawking this statistic intentionally lead their viewers to assume, “arrested for murder” is equivalent to “guilty of murder.” And that 50% of the murder arrests is equivalent to 50% of the total murders. The entire demographic is assumed to be more dangerous.
The thing about this is that the kind of people who quote statistics like that typically don’t have an interest in all of that. They start with a racist assertion, then search for anything that appears to corroborate. They have no interest in actually understanding the statistic, they only care about it insofar as they believe it justifies their racism.
That, or they know it doesn’t and they’re purposely arguing in bad faith.
Yeah… that’s a pretty reasonable conclusion. It’s hard to just state outright though, when I live with the exact sort of person described in your comment.
It’s interesting: the people who are fine with calling an entire race murderous seem to take great umbrage at being considered “racist.”
It’s the r-word to them – a slur used to invalidate their concerns and diminish the importance of their well-being.
That their concerns ought to be invalidated – since they are the racist result of racist fear-mongering – is never well-received.
One of my favorite Brian Regan bits kinda fits, maybe?
“In 1939, Germany invaded Poland. One thing led to another and the United States of America dropped two atomic bombs on the sovereign nation of Japan.”
Clumsy. Did they at least pick them up on the way out?
In places where more storks live, you also have more babies.
After the Corona lockdowns there was an increase in infections with the common cold. Researches tried to explain how this is connected to the immune system and a lot of people now assume you have to “train” your immune system with exposure to pathogens. Or that your immune system falls out of training (like a muscle) if you stop exposing it to pathogens regularly. A potentially dangerous misunderstanding.
People often draw false conclusions from reduced information about a fact. For example: Babies who are kept in one position for hours each day over weeks or months show developmental delay. For some reason this information got shortened so much that a lot of people (in Germany at least) now assume baby seats are hurting babies backs.
Of the ~100 billion humans who have ever lived, about 8 billion (8%) are still alive today. Therefore, your chance of dying is 92%, not 100%.
“Vending machines are more deadly than sharks”.
While it’s true that (at least for some years) more people are killed by vending machine accidents than shark attacks, your personal risk depends on what you do. If you’re a vending machine factory worker who never goes into the ocean, you’re far more likely to be killed by a vending machine than a shark. But if you live in a part of the world that doesn’t have vending machines and you swim in the ocean every day, the reverse is true.
When people say a politician “raised taxes.” More often than not it’s a tax that does not apply to 99.99% of the population and they raised it from 0.000001% to 0.000002%
But boy do those campaign ads look good
Similarly, when a politician says they cut taxes, middle class tax cuts are almost always intend to “sunset”. That is, eventually, those tax cuts are designed to reverse themselves over time.
Women have smaller brains than men.
I mean, yes. Women as a population are physically smaller than men as a population.
Women have smaller fingers than men. Smaller eyes. Smaller lungs. There is no “gotcha” that smaller skeletal frames with smaller skulls contain, by volume, a smaller organ.
Doesnt mean every man’s brain is larger than every woman’s brain either.
Doesn’t mean men are smarter than women.
It’s just a statistic, that while true, doesn’t imply what some people think it does.
Many years ago I worked as an analyst at a small VC firm. My boss, who was a raging misogynist prick and liked to date College freshmen, LOVED this fact (and any other Manosphere bullshit he could find about women being inferior to men). He was such an unbelievable stereotype, he could have stepped out of a sitcom.
Yeah I mean, neanderthals had bigger brains than humans, and they were no smarter than we are (as far as we know.)
Also a blue whale’s brain is four times the size of a human brain and they don’t even know how to drive.
We don’t really know where blue whales go a lot of the time, so I think that’s a bit of an assumption there.
I’m assuming down.
When you think about data it actually gets really scary really quick. I have a Master’s in Data Analytics.
First, data is “collected.”
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So, a natural question is “Who are they collecting data from?”
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Typically it’s a sample of a population - meant to be representative of that population, which is nice and all.
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But if you dig deeper you have to ask “Who is taking time out of their day to answer questions?” “How are they asked?” “Why haven’t I ever been asked?” “Would I even want to give up my time to respond to a question from a stranger?”
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So then who is being asked? And perhaps more importantly, who has time to answer?
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Spoiler alert: typically it’s people who think their opinions are very important. Do you know people like that? Would you trust the things they claim are facts?
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Do the data collectors know what demographic an answer represents? An important part of data collection is anonymity - knowing certain things about the answerer could skew the data.
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Are you being represented in the “data”? Would you even know if you were or weren’t?
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And what happens if respondents lie? Would the data collector have any idea?
And that’s just collecting the data, the first step in the process of collecting data, extracting information, and creating knowledge.
Next is “cleaning” the data.
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When data is collected it’s messy.
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There are some data points that are just deleted. For instance, something considered an outlier. And they have an equation for this, and this equation as well as the outliers it identifies should be analyzed constantly. Are they?
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How is the data being cleaned? How much will it change the answers?
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Between what systems is the data transferred? Are they state-of-the-art or some legacy system that no one currently alive understands?
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Do the people analyzing the data know how this works?
So then, after the data is put through many unknown processes, you’re left with a set of data to analyze.
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How is it being analyzed? Is the analyzer creating the methodology for analysis for every new set of data or are they running it through a system that someone else built eons ago?
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How often are these models audited? You’d need a group of people that understand the code as well as the data as well as the model as well as the transitional nature of the data.
Then you have outside forces, and this might be scariest of all.
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The best way to describe this is to tell a story: In the 2016 presidential race, Hillary Clinton and Donald Trump were the top candidates for the Democratic and Republican parties. There was a lot of tension, but basically everyone on the left could not fathom people voting for Trump. (In 2023 this seems outrageous, but it was a real blind spot at the time).
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All media outlets were predicting a landslide victory for Clinton. But then, as we all know I’m sure, the unbelievable happened: Trump won the electoral college. Why didn’t the data predict that?
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It turns out one big element was purposeful skewing of the results. There was such a media outrage about Trump that no one wanted to be the source that predicted a Trump victory for fear of being labeled a Trump supporter or Q-Anon fear-monger, so a lot of them just changed the results.
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Let me say that again, they changed their own findings on purpose for fear of what would happen to them. And because of this lack of reporting real results, a lot of people that probably would’ve voted for Clinton, didn’t go to the polls.
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And then, if you can believe it, the same thing happened in 2020. Even though Biden ultimately won, the predicted stats were way wrong. Again, according to the data Biden should have been comfortably able to defeat Trump, but it was one of the closest presidential races in history. In fact, many believe, if not for Covid, Trump would have won. And this, at least a little, contributed to the capital riots.
Oh yeah. I might say some wrong stuff since I’m quite ignorant but. Statistics is messy and I tend to avoid including too much stats in my projects, although sometimes I accidentally end up blindly doing so and believing them also drawing inaccurate conclusions. Physical stats are even messier because not everybody has the competence to accurately understand what they mean, or sometimes we just don’t understand the world enough. Environmental science data is an example of that. I rely on other people’s analyses cause I can’t read them. I don’t know much about politics.
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The large percent of traffic accidents that take place within 5 miles of home. Most people only cover a fairly small radius on a day to day basis so it makes sense if there is an accident, it’s close to home and not 80 miles away… just on average of how far how often you drive. Makes it seem like neighbourhoods are more dangerous than highways or something.
that is actually an interesting way to think about it
This is minor one, but annoys me how comnmon this is: light is made out of litle packets of energy called photons.
Here is a good video on the topic: https://youtube.com/watch?v=SDtAh9IwG-I (Too lazy didn’t watch: Light is an electromagnetc wave and is is not quantized. Only the interactions between atoms and light are quantized)
huh, I thought quantization of light(or energy really) came from Heisenberg’s uncertainty principle
