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    Randomness is Random - Numberphile

    Numberphile

    4 HN recommenders · recommended in 4 months, 2019–2022

    YouTube

    13:31 · 893K views · Dec 18, 2018

    Numberphile with Simon Pampena on how humans misunderstand randomness, including coin-flip sequences, run lengths and the gambler's fallacy.

    Recommended as an accessible demonstration that people are bad at producing or judging random sequences.

    Mathematics & Theoretical CSProbability & game theoryRecreational & visual math
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    4 comments from Hacker News

    1. @codesushi42Jun 2019· in “Interview on ”Bayesian Statistics the Fun Way””

      I would argue that the problem with frequentist statistics is that it aligns with humans' flawed intuition of how randomness works. People are inherently obsessed with finding patterns to support their hypotheses. The problem is that what we perceive as random and extremely unlikely events are in fact much more probable than what we estimate from using Gaussian methods. And the frequentist approach helps to create this distortion by ignoring black swans. Here's a great video demonstrating how people tend to misunderstand randomness: this video

      RecommendsView on HN
    2. @reidacdcJan 2019· in “Perceptions of Randomness: Why Three Heads Are Better Than Four (2009) [pdf]”

      There is a very accessible "numberphile" video which I think is closely related to this, about playing the odds on predicting elements of a nominally-random coin-flip sequence using the expected run length. this video

      RecommendsView on HN
    3. @wruzaSep 2019· in “A New Algorithm for Controlled Randomness”

      Oh, I remember good old fallout 1-2 when you have 42% hit chance but still miss 7 times in a row. There is also a good Numberphile video on what we think random really is (it is not) this video

      RecommendsView on HN
    4. @throwamonFeb 2022· in “Some neurons are active when adding, others when subtracting”

      This may indirectly answer your question: this video TLDW: We don't pick numbers at random. At all. We're so bad at it that you can make money off this fact.

      RecommendsView on HN