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Since the invention of seatbelts there have been a larger number of serious injuries from car accidents.
This sounds like seatbelts are causing serious injury but in fact, these serious injuries used to be deaths. That statistics is never mentioned causing it to be misleading, just like they never mention how many bugles are in the car when an accident happens
Oh how I miss the before times.
Counterpoint: True Facts is a great series of humorous nature documentaries.
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.
Switching from a 5mpg truck to a 10mpg truck does more for the environment than switching from 40mpg car to a 55mpg car.
How is that misleading, isn't it true?
The ask was
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?
More outrageous sounding, switching from a 5 mpg truck to a 10 mpg truck saves more gas than switching from a 50 mpg car to a 100 mpg car
I still don't understand hot that statement is "misleading"?
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
Environmental damage from emissions doesn't care about relative efficiency, 15 free miles is objectively more than 5 free miles.
but it's not like a person in a 50mpg car is likely to drive 5 times as much per year as the person in a 10mpg truck. over consistent distances, improving the shitty mileage vehicle will save a lot more gas.
swapping a 5mpg truck for a 10mpg truck will save 10 gallons per hundred miles, while switching a 40mpg car for a 55mpg car will only save 0.68 gallons per hundred miles. even going from 5mpg to 6mpg would save more than that.
It you travel 50 miles at 5mpg, you use 10g of fuel At 10mpg you use 5g...a saving of 5g
40mpg uses 1.25g 55mpg uses 0.91g a saving of 0.34g much less of a saving.
Yeah but if youβre already driving the more efficient vehicles to begin withβ¦
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
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%.
Thunderstorms & lightning strikes can severely affect "cloud" computing!
Each year, Dihydrogen Monoxide is a known causative component in many thousands of deaths and is a major contributor to millions upon millions of dollars in damage to property and the environment. Some of the known perils of Dihydrogen Monoxide are:
Death due to accidental inhalation of DHMO, even in small quantities.
Prolonged exposure to solid DHMO causes severe tissue damage.
Excessive ingestion produces a number of unpleasant though not typically life-threatening side-effects.
DHMO is a major component of acid rain.
Gaseous DHMO can cause severe burns.
Contributes to soil erosion.
Leads to corrosion and oxidation of many metals.
Contamination of electrical systems often causes short-circuits.
Exposure decreases effectiveness of automobile brakes.
Found in biopsies of pre-cancerous tumors and lesions.
Given to vicious dogs involved in recent deadly attacks.
Often associated with killer cyclones in the U.S. Midwest and elsewhere, and in hurricanes including deadly storms in Florida, New Orleans and other areas of the southeastern U.S.
Thermal variations in DHMO are a suspected contributor to the El Nino weather effect.
Every single rapist and murderer was found to have dihydrogen monoxide inside their body at the time they committed their crimes, and your friends and family may be using it recreationally without you knowing
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.
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, itβs close to home and not 80 miles awayβ¦ just on average of how far how often you drive. Makes it seem like neighbourhoods are more dangerous than highways or something.