Statisticians like me say CORRELATION ISN'T CAUSATION but that's not the whole story.
There are at least FOUR different scenarios!
A thread. 🧵
1. CORRELATED BY CHANCE. There's always a possibility that variables will correlate by chance. If you have a lot of data, you're almost certain to get a few high correlations. You will know you're in this situation if the same variables are much less correlated in new data.
2. CORRELATED DUE TO STRUCTURE. Clocks are correlated with each other but there's nothing about Clock A that can be changed in order to cause a change in Clock B or vice versa. There is no third thing you can change that will cause both clocks to change. There is no causation.
You might be tempted to say that the clocks have the common cause of being created by humans. Imagine two random stars that have a cyclical change in brightness every 24 hours. They will be correlated as well. It's not about who created them. It's about their similar structure.
3. MURKY CAUSATION. In the simplest case, if A and B are correlated and there is some causation then this could mean that A causes B, B causes A or some third thing C causes both A and B. In the most complex case, there could be complicated feedback loops between A and B.
In these cases, when we say "correlation isn't causation", what we mean is that we can't identify exactly what kind of causation there is but there is some.
4. EVEN MURKIER CAUSATION. A and B might not be related at all in the real world but something about your data collection may have caused data about A to be related to data about B. Technically, you could say you or your data collection are the cause of the correlation.
However, in the context of the original variables themselves and the real world, A is not causally related to B.
Hope this was educational! 🧵
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One of the things I hate most about the cult of IQ is it leads to lot of magical thinking about how the brain works. There’s absolutely nothing shameful about relearning things you use to know.
Research shows forgetting is a normal part of human cognition.
The way to combat the natural tendency to forget is to relearn or retrieve the memory at regular intervals which is known as “spaced repetition”.
Relearning takes the strength of the memory back to 100% and the rate of forgetting is slower the next time.
Infographics of this dataset have been kicking around on the internet for years. It is an insult to real scientists everywhere. For every 10 likes, I will post a new ridiculous fact about how fake and ridiculous this "data" is.
They report data on 185 countries but *104* of those numbers (more than half!) are based on *zero* data collected from people from that country. ZERO.
Rather than acknowledge this lack of data, they decided to guessimate based on surrounding countries.
The IQ estimate for Equatorial Guinea was based on kids in a home for developmentally disabled kids living in Spain. Not even their home country. Spain.
People are getting thousands of likes for spreading this misinformation about sex differences. Let me explain why this interpretation of the data is wrong. 🧵
If you think 100% accuracy is too good to be true, trust your instincts.
The version of the model shown in the plot was basically fed the sex of the participants. That’s why it’s achieving 100% accuracy.
When the model was tested on a subset of people from the same dataset that it had *not* seen previously, the accuracy fell to 90%.
I keep seeing this Huberman clip all over my timeline so let’s use it as teachable moment to learn some statistics.
The basic mistake is not taking the people who are already pregnant out of the pool of people who could be pregnant the next month. Of the starting 100, fewer and fewer will remain each month.
It’s a little tedious to keep track of what number of people aren’t yet pregnant on each round, and then take 20% of that, and then add up all the pregnant people in each round.