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.
Correlation at least tries to imply they’re related. As lottery sales go up in your household so does credit card debt. Not always a cause but they’re related
You’re looking for spurious correlations which is when numbers have no business even being used in a comparison
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 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.
Well a lot of people would think gaining 50 mpg is way better than gaining 5 mpg, since it’s 10x as much, but really it just shows that you can’t use mpg as a unit to compare like that
This is why the rest of the world uses l/100km (liters per 100 kilometers), the comparison is linear and thus comparable between different vehicles in a simple manner.
5mpg = 20g/100mi
10mpg = 10g/100mi
40mpg = 2.5g/100mi
55mpg = 1.82g/100mi
The difference between 10 and 20g is easy to see as a lot bigger than the difference between 2.5 to 1.82g. 15 is a much bigger number than 5, but that 15 is relative to the initial mpg rating
In fact going from 5mpg to 10mpg is better than going from 10mpg to 100mpg, a 10g saving vs a 9g saving…the more you know
but if we are trying to save the world getting the lowest mpg vehicles off of the road first will have a stronger effect
if you already drive a 30mpg car and you are ready to upgrade then definitely look for better efficiency but I think we should have incentives in place to get cars that operate at for instance 16 mpg (my first car for instance, 1996 Chevy blazer, now deceased) replaced by even 10 year old models which are much more efficient
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.
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%.
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.
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.
Yup, I had to explain this to so many people when I sold coffee. Nobody believed me at all. I explained that dark roast had more of the caffeine cooked out of it.
When you think about data it actually gets really scary really quick. I have a Master’s in Data Analytics.
First, data is “collected.”
So, a natural question is “Who are they collecting data from?”
Typically it’s a sample of a population - meant to be representative of that population, which is nice and all.
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?”
So then who is being asked? And perhaps more importantly, who has time to answer?
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?
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.
Are you being represented in the “data”? Would you even know if you were or weren’t?
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.
When data is collected it’s messy.
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?
How is the data being cleaned? How much will it change the answers?
Between what systems is the data transferred? Are they state-of-the-art or some legacy system that no one currently alive understands?
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.
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?
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.
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).
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?
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.
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.
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.
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.
Add comment