How We’re Fooled By Statistics
Why Hacker News recommends it
HN commenters link it as the clearest short explanation of regression to the mean, especially for how it misleads people about feedback and statistics.
What it is
A short Veritasium video explaining regression to the mean and how ignoring it leads to wrong conclusions about punishment versus reward, medical treatments and sports streaks. It uses the example of flight instructors who believed criticism works better than praise.
Recommended by 3 HN commenters across 3 different months, 2014 to 2025.
What Hacker News said
Strong rec np422 · 2015-03 · on “Speed Cameras and the City”
Or perhaps it's just the old phrase "Lies, damned lies, and statistics" that is appropriate once again? Veritasium explains it better than me. Please watch [this video]
Recommends javajosh · 2014-08 · on “Set Up to Fail: How Bosses Create Their Own Poor Performers (1998) [pdf]”
>Recognizing people's successes and what they are doing well is a 1000% more effective than harping on what people have messed up Yes, this is (verifiably) true. One must be aware, however, that positive feedback is usually going to be ineffective, thanks to natural "regression to the mean" effects, elucidated wonderfully in this Veritasium video[1]. (The point, in the end though, is that performance is going to statistically improve if people do worse than average, and do worse if they do really well, regardless of the feedback you give them", which is called "regression to the mean". The net effect is that you might get a false positive for negative feedback, and a false negative for posit
Recommends divan · 2025-04 · on “How to win an argument with a toddler”
> results are so obviously and immediately measurable Ehm… not really – especially not the "obviously" part :) Controlled or even abusive coaching can sometimes lead to better short-term results, but often at the cost of athletes’ mental health and long-term performance. What’s worse, coaching culture in many countries falls victim to the "regression to the mean" fallacy. I’m sure HN readers are familiar with it, but most coaches aren’t – and they’re not trained to adjust their intuition accordingly. Coaches tend to praise athletes when they perform well and criticize them when they don’t. But statistically, if an athlete has an unusually good day in practice, they’re likely to perform worse
Cites sillysaurusx · 2021-05 · on “Diffusion Models Beat GANs on Image Synthesis”
is that actually the case? [are model weights being published less frequently?] It sure feels like it. But it could be survivorship bias: [LINK] or regression to the mean: [this video] . It's possible that StyleGAN and GPT-2 just happened to coincide, and both companies just happened to decide to release their models rather than hoard them, and that the usual case is for companies to rarely (if ever) release weights with their research. But the more interesting question is, why wouldn't they? So onward to your second one: are ethical concerns actually the reasons some models/weights are not released? Or is it about hoarding a potentially valuable commodity? Or some other reason, or a combination