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16 votes
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Eight basic rules for causal inference
9 votes -
Genomic prediction of IQ is modern snake oil
11 votes -
The Dunning-Kruger effect is autocorrelation
30 votes -
Can YOU win rock, paper, scissors against Grey? 99.9999999% will fail.
40 votes -
Can anyone recommend a specific type of statistics course?
I would like to find a good Statistics course to do for myself, and also to recommend to others, down the road ... one that specifically focuses on risk, and the discrepancy between actual...
I would like to find a good Statistics course to do for myself, and also to recommend to others, down the road ... one that specifically focuses on risk, and the discrepancy between actual statistical probability vs humans' intuitive sense of risk.
I recall a quote, which The Interwebs informs me right now, came from Albert A. Bartlett ... "The Greatest Shortcoming of the Human Race Is Man’s Inability To Understand the Exponential Function".
Alternately, Mark Twain popularized (but did not originate) the saying "There are lies, damned lies, and statistics".
That's the kind of course I'm looking for, that focuses on questions like how much should we actually worry about supervolcanoes, asteroid strikes, Covid 2.0, WWIII, Trump getting re-elected, etc.
There are two parts to this. One, people often (naturally, human nature, how our brains are wired to handle Risk) obsess about a short list of risks in life that are overblown, or appear to be more of a concern than they actually are.
The other part is, some things have a very small risk of actually happening, but when considered in conjunction with the potential consequences (asteroid strikes, WWIII, global pandemic), are still worthy of aggressive efforts to prevent ... and people often focus on the first element (statistically unlikely) and dismiss or overlook the second piece (devastating consequences).
Anyway, stuff like that ... ideally an actual, hands-on MOOC-type Statistics course, but even a good youtube video or blog article would suffice.
As usual, thanks in advance.
5 votes -
On the hunt for ginormous effect sizes
5 votes -
‘Big’ data can be 99.98% smaller than it appears
11 votes -
How hard is it to get counting right?
3 votes -
If correlation doesn’t imply causation, then what does?
11 votes -
How eugenics shaped statistics
9 votes -
How to think like an epidemiologist
6 votes -
The Monty Hall problem
22 votes -
Arvind Narayanan: How to recognize AI snake oil
4 votes -
Big data+small bias << Small data+zero bias
5 votes -
Some of the Pew Research Center's most noteworthy findings from 2019
9 votes -
Grok Correlation
4 votes -
AI competitions don’t produce useful models
5 votes -
The why of the world
2 votes -
It's the Effect Size, Stupid - What effect size is and why it is important
9 votes -
It’s time to talk about ditching statistical significance
19 votes -
Does Hollywood ruin books? - Numberphile
11 votes