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.

https://www.dhmo.org/facts.html#DANGERS

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.

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.