Imagine somebody tweets that they think men on dating apps are jerks.
A commenter asks, "Do you have any peer-reviewed publications to back up your claim?"
The tweeter says they don't.
The commenter then accuses them of knowingly spreading misinformation.
I call this kind of thing a "claim escalation" and I think it's usually a jerk move. The original person tweets something that we all know to take with a pinch of salt. But responders pretend the the tweeter's claim is more than it is as a way of silencing their perspective.
Here's another example. Imagine somebody says that when they have a tummy ache, they find that warm soup stock often helps. If this person has no medical basis for this claim, would we be justified in calling them out for spreading potentially dangerous medical misinformation?
My intuition is that in the case of the soup stock, it's just a bit of harmless talk. My intuitions flip the other way if the person was recommending soup stock as a cure for covid-19. In that case, I would definitely like for that person to have references for whatever they say.
Where do you think the line should be between being a jerk and providing a valuable public service by pushing back against misinformation?
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Your rapid test is positive. Does that mean you have covid?
Here's the math you need to figure it out. A thread. 🧵
Let's define some terms that you might have heard of.
A "true positive" means the test result was positive and the person tested does indeed have covid.
A "false positive" means the test was positive but the person doesn't have covid.
A "true negative" means the test was negative and the person tested doesn't have covid.
A "false negative" means the test was negative but the person does have covid.
Got it? Good!
How did I get this poll with almost 29k responses to balance perfectly? A thread. 👇
Assuming most people didn't secretly flip a coin, where's the randomness in the poll coming from? I think it comes from three sources:
1. Some folks were genuinely picking randomly
2. Based on the comments, even for folks who used a system, the method they used was very unique to them and therefore really random relative to other people
Here’s the result of yesterday’s statistics experiment!
The poll is significantly 😉 biased!
WHY???
A thread.👇
Here’s my plot of the responses as they came in.
With 7291 responses, this is *really* baised. The chances of it being a “fair coin flip” are basically 0. 😂 What’s going on?
As a good data scientists, we can use our qualitative data to help us understand our quantitative data! What qualitative data? The comments! Apparently, some folks tried to think one step ahead of the other respondents.
For a lot of people, mathematics is true in the same way that "Kermit The Frog and Miss Piggy are a couple" is true. It's true in an imaginary world where we have agreed upon rules. If that's how you think about math then it's pretty obvious that "2+2=4".
To me, "2+2=4" means that "2 things + 2 things will always be 4 things no matter what the things are". Turns out this is not technically true. You can create all kinds of mathematical systems and physical situations where 2 things + 2 things is not 4 things.
Just found this. Not sure if @michaelshermer is confusing @nhannahjones with me or somebody else because I never said most of that stuff either. What I will say is I learned from my (mostly white) grad school professors how to construct mathematical systems where 2+2 isn't 4.
If that seems contrary to reason to you then I humbly suggest that maybe you don't understand reason as well as you think you do. I know many of us probably learned in grade school that 2+2=4 but the relevant context is it's basic math that they teach to kids.
My race seems to suggest to people that this is a race thing somehow. It's not. Check out the link for a PhD who's not black and who also agrees that 2+2 is not always 4. As Dr. Hossenfelder puts it, "It's not woke. It's math."
Are you frustrated with how organizations like the CDC and the WHO are handling the pandemic? Do you wish they did a better job of following the data?
If so, read on... 👇
One of the earliest lessons of the pandemic was covid outbreaks can get really bad really quickly. While the costs of over-responding are easy to predict like unnecessary financial losses and physical discomfort, the costs of under-responding are harder.
Some areas got away with relatively small outbreaks. Others experienced tremendous disruptions to their healthcare system and significant losses of life.