Very interesting take from @StatModeling :
“The social sciences are useless. So why do we study them?”
He argues that the main benefit of good social science research is to push back against the bad social science that people would otherwise do.
I’m sympathetic to this argument. Social science does provide a strong pushback to amateurish analyses of race and gender related data for instance which are very common on social media.
I think sometimes people are little too quick to claim that based on FACTS and LOGIC other people's risk preferences don't make sense.
For instance, I frequently see the claim that it's hypocritical or unreasonable to be willing to participate in a high-risk activity but then not be willing to participate in other lower risk activities.
The idea seems to be that a fully rational person would have some kind of fixed risk threshold. Below the risk threshold, the person would be willing to participate in the activity assuming it's something they find worth doing. Otherwise, they would decline.
Never confuse "sensitivity" and "specificity" again!
Here's how I keep them straight. 👇
First some background. There are four categories of test results:
[Skip this tweet if you already know this part!]
TP: Tested positive, Infected
FP: Tested positive, Uninfected
FN: Tested negative, Infected
TN: Tested negative, Uninfected.
Sensitivity is how "sensitive" the test is meaning "does it pick up all the cases?" What's another name for the cases detected by the test? The true positives! Therefore, another name for the sensitivity is the *true positive* rate. It's the percent of cases detected by the test.
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!
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?
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.