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.
It’s hard to say if all this over-thinking is what biased the results. Maybe it was the order of the responses that caused a bias. Personally, I doubt order matters much on Twitter because I’ve done polls where the options were pretty similar and the poll ended up being 50-50.
So my best theory for now is my statistically savvy followers tried to out-think the group and ended up biasing the poll! This is a fun outcome! Much more interesting than if it was a boring 50-50 result. 😁
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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
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.
Lets clear up some things about:
- race
- social constructs
- biological constructs
- sociological causation
- biological causation
- predictive accuracy
A thread. 1/n
Let's say X predicts Y. This doesn't mean X is in any way causally related to Y. Therefore, if I say "X predicts Y", it doesn't mean I'm saying "X causes Y". So in particular, if I say race predicts a health outcome, this doesn't mean race caused that outcome. 2/n
The next thing we should talk about is causation. Sociological causation involves entities from a sociological framing of the world. Biological causation involves constructs that originate out of a biological framework. 3/n
Speaking as a black statistician, I don't think we can completely eliminate using race in medical decisions if we want to make the best decisions for each patient given our current state of technology. Gene testing for specific ancestry would be better but we aren't there yet.
Leaving out race can have a lot of unintended consequences. Algorithms might default to the standard of care for the largest group. This means treating everybody as if they are white which would be problematic in many cases.
Alternatively, algorithms may relearn race from the data. They will use family history, geography and other demographic variables to guess the race. This can be tricky to detect if you aren't looking for it. "Racist" algorithms get released to the public all the time.