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?
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.
According to a new study published by the University of Berchul, eating ice cream can make you be in risk of drowning.
So there’s some “incorrect” assumptions you have made about the North American summer, and weather in Africa. In the North American summer, only North Africa experiences summer with you guys. The rest of the continent is blanketed in rains (West, Central and East Africa) or are in outright winter (Southern Africa). So our temperatures do come down in your winter. Your coldest months are our hottest months for most of the continent (except for North Africa). So saying the developed parts of Africa
In equally unrelated news, there’s also a direct correlation between ice cream sales and shark attacks. We have to steal all the ice cream before more people get eaten!
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%.
Yeah that statistic is brutal. Like I wish more people understood it’s like saying: “we bully the shit out of people who seem depressed, we aggressively stigmatize antidepressant use, X% of people with depression will attempt suicide at some point in their lives. We should ban antidepressants and treat depressed people worse.”
Man, I can’t believe we live at a time where being trans is more dangerous than having cancer…
Its so frustrating when I see other minorities use that argument because their suicide statistics are also typically higher! That’s the nature of oppression.
“Numbers don’t lie” is true in the same sense as “guns don’t kill”…
Numbers don’t lie, but people lie using number all the time.
Hey vis4valentine, you should correct “wish” to “which” in your comment. That typo could cause readers to understand the sentence completely inverted.
I learned that stats is all about lies lol
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.
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?
Nate Silver was singing a different tune, though. I remember an interview he gave a month out from the election where he noted significant softness in support for Clinton. There were also a lot of undecideds who might swing elections in key states. That is, of course, exactly what happened. When the Comey letter was leaked by Congress, it likely cost Clinton the election. Her poll numbers dropped from +7% to +3%, well within the advantage that the Electoral College gives to Republicans.
On Election Day, the 538 model was about 3:1 in favor of Clinton. That sounds highly in favor of Clinton, and it is. But it still leaves plenty of room for a Trump win. And lo and behold, she lost.
That’s interesting, I did not think the letter had that big of an impact.
For me it was Bernie. I remember a lot of us on Reddit were all about Bernie.
Iirc, Bernie had a lot of steam and it seemed like again Clinton was going to be pushed aside for a grass-roots candidate (just like with Barak years earlier).
And Bernie said he was not going to give up the race, because even if he didn’t win the votes he could still be voted in at the national convention.
And as the DNC neared, things were looking great. Clinton was giving paid speeches to wall street and Bernie was tearing her whole campaign apart because he was saying, give money back to people and she was saying keep things the way they were.
And then, among mounting pressure, two weeks before the convention he concieded out of nowhere. At least that’s what it seemed like to us.
Then emails leaked that showed the Democratic Party had colluded with Clinton to get Bernie out of the race!
We couldn’t believe it. We were devestated. So some people went to the DNC and were making a big stir, demanding that Bernie get back on the ballot.
And it all came to a waterfall moment when Sarah Silverman was on stage. And people were chanting Bernie and she lost it and told everybody to shut up and said the Bernie supporters were stupid.
And that was it. The only thing that came out of it was somebody got fired, but there was no regard or representation for us in the Democratic Party anymore.
They didn’t care about what we wanted, and they were just as crooked as they had always told us the Republicans were.
For me it was a massive dissolutionment, and drove me to Trump. Since he was saying we need to take our economy back from the 1%.
I won’t say Bernie supporters weren’t a factor, but the prospect of “buttery males” was an easily measurable factor. Trump was having a really rough few weeks running up to the election. He had a piss poor debate showing, the Access Hollywood tape, and sexual assault allegations all coming together against him. Even with Russia laundering their hack of John Podesta’s emails through Wikileaks and Wikileaks working working with the Trump campaign to drip out the hacks, Trump was well behind. It was hard to see anything with Bernie supporters because that played out over the entire campaign. Meanwhile, the Comey letter had an immediate effect over mere days.
Clinton was giving paid speeches to wall street
Note that Clinton’s speeches were from well before the campaign. When I looked at the transcripts when they got released as part of the Russian hacking, I could see why she didn’t want them released. There were parts where she was being more frank about certain subjects than politicians usually are. It was easy cherry pickings from there. And as much as the paid speech circuit has its detractors, I’d rather see former or dormant politicians giving empty platitudes to rooms full of bankers than lobbying their former colleagues.
she was saying keep things the way they were
At the very beginning of Hillary Clinton’s campaign, she did a tour of the nation and just listened to people’s problems and concerns. From there, she drew up a platform. She has a history of doing this sort of thing like when she was a senator in New York, where she tackled loss of jobs in upstate New York in areas that had been ignored.
She also was pretty blunt with certain areas, like talking in West Virginia about needing to plan for a future after coal. To his credit Bernie didn’t jump in there to attack her, but he also didn’t exactly jump to cover the subject. Trump of course did, lied to the workers, got their votes, and they’re still losing jobs anyway.
And it all came to a waterfall moment when Sarah Silverman was on stage. And people were chanting Bernie and she lost it and told everybody to shut up and said the Bernie supporters were stupid.
She shouldn’t have lost it, but I can see why. I remember Bernie supporters in general getting extremely annoying around that time. It’s the same attitude that we saw out of Trump supporters: everyone I know loudly supports Bernie/Trump, no one I know supports Clinton/Biden, therefore I was cheated. I couldn’t poke my nose up on /r/politics in support of Clinton without getting my face gnawed off.
And that was it. The only thing that came out of it was somebody got fired, but there was no regard or representation for us in the Democratic Party anymore.
There was supposedly a takeover of the DNC by the Clinton campaign. This is a questionable interpretation. tl;dr: A heavily indebted DNC traded fundraising by the Clinton campaign for some control. Nothing stopped Bernie from a similar deal. Also Donna Brazile told the Clinton campaign that there would be two questions: one on capital punishment and the second on lead in drink water. I’m sure she had a stock answer for capital punishment. For the second, the town hall was in Flint, Michigan. Yeah, of course they’re going to ask about lead.
For me it was a massive dissolutionment, and drove me to Trump. Since he was saying we need to take our economy back from the 1%.
Did Trump ever actually say that? I ask the question because Trump does this thing where he leaves himself as a blank canvas. Two supporters with different values can believe contradictory things about Trump without there actually being evidence of a contradiction because he either never said anything or because he just says things without meaning them.
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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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.
There’s actually some historical context for this untrue way of thinking.
France, 1873 Paul Broca, a French physician, decides to weigh some brains. And women’s brains weighed less than men’s brains. This is part of his research into crainiometry in which the size of the brain is used to understand a mesure of intelligence. Bigger brain weight = more smart.
We now recognize crainiometry as a pesudoscience.
Then another French academic Gustav Le Bon uses Broca’s research to further engain that not only are women’s brains small causing them to have the big dumb, women are in fact more similar to gorillas in brain size. Thus, women are uncivilized, akin to children, and MUST be under the care and control of men who are CLEARLY more intelligent with their big brains and, naturally, should control and run society.
Broca did not take overall body size or age of the specimens into account when originally weighing the brains. The male specimens were younger and larger to the female specimens who were smaller and older. Brains tend to shrink as we age.
So, not only was this flawed science, based in flawed measurements, thay have been readily disproved, we’re still struggling to undo this as a belief.
History rant over.
Men have bigger balls is another missunderstood fact.
Gonna need a source on that claim
The average human has less than 2 arms.
And half a penis.
On average, humans have just under 3 inches of penis.
ceiling(AvgArms)
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.
Another factor is that people feel more comfortable driving their local roads and get used to usual traffic patterns, which could mean that they’re not as alert if something’s different.
Eg you’re almost home, in your neighborhood, and there’s a stop sign that almost never has anyone else there, so you might not look too much just roll through, the one time someone’s actually there.
This is such a good example for how statistics are often misinterpreted without any fault of the statistics itself.
It reminds me of when they looked at fighter jets to decide which parts to reinforce. So they examined which parts had the most bullet holes and came up with this statistic:
If some of you don’t knew about this yet, I let you decide why this effect is called “survivorship bias”. :D
There needs to be more education about how statistics need to be looked at in the correct context.
There are better examples of survivorship bias, but simce this one deals with war and comes with an easy to understand picture, people rarely remember the other examples so only this one ever gets posted.
that is actually an interesting way to think about it
“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.
Wait, so you’re telling me that there are no vending machines in the ocean that are preying on people swimming in the water?
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However sharks have a huge PR issue and Spielberg regrets how Jaws is a big source of that
‘true fact’.
- Facts cannot be anything except for true.
- Anyone who uses the two words ‘true fact’ together cannot be trusted because they know neither the meaning of the word ‘true’ or the word ‘fact’.
I can’t trust you on this because you are using the words ‘true fact’.
Facts are just objective statements, which can be either true or false, but whichever they are it is objective and not dependant on the observer.
I mean, it’s a semantic argument, and semantics is subjective, but that’s probably how the people who say ‘true fact’ are defining fact.
Who called the tautology police?
Light roasted coffee has more caffeine than dark roasted coffee.
Technically, per bean, more of the caffeine is cooked out of the dark roast. However, other things are also roasted out of a dark roast to the point that the individual beans are also lighter and smaller. When brewing coffee, usually you either weigh your dose of beans out, or you use a scoop for some consistency. Either method will result in more dark roast beans ultimately making it into the brew than would with a (larger, heavier) light roast.
Typically, this more than cancels out the reduced caffeine content per bean, so a brew of dark roast coffee still typically has more caffeine in it.
If I remember correctly, dark roast was also originally devised to hide bad-quality coffee beans. Nowadays it is often implied that darker roasts are better, which actually isn’t necessarily the case.
Implied where? All the coffee snobs I know drink lighter roasts and derogatorily call dark roasts “supermarket coffee”
Can confirm. Source: am coffee snob.
Dark roasts have a more consistent taste/flavor and it has a longer shelf life, so it’s easier to know what you’re getting. If you want to taste the variety of flavors coffee can have, you’ll go for fresher lighter roasts.
James Hoffman did a great video on this, and yes, kinda. It’s complicated.
This is actually very interesting and I had no idea. Thanks!
This is actually very interesting and I had no idea. Thanks!
This is actually very interesting and I had no idea. Thanks!
This is actually very interesting and I had no idea. Thanks!
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%.
Are you assuming that everyone currently alive is immortal? You may be in for some disappointment.
That’s what’s misleading about it.
Yeah, only about 8% of people currently living are immortal, so don’t get your hopes up.
Classic, but very illustrative
People reading this, aren’t you just a ray of sunshine with your 8% survival rate?
Thunderstorms & lightning strikes can severely affect “cloud” computing!
Switching from a 5mpg truck to a 10mpg truck does more for the environment than switching from 40mpg car to a 55mpg car.
And this is why l/100km is a better unit
Nice try, QI elf!
Several (attempted) murderers have owned copies of The Catcher in the Rye.
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.
