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  1. proslijedio/la je Tweet
    13. sij

    Amazing resource to make sense and the most of reaction time distributions:

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  2. proslijedio/la je Tweet
    9. ruj 2019.

    Impostor syndrome is so pervasive because every single one of us got lucky somewhere along the way. The myth of meritocracy is bullshit: talent is equally distributed but opportunity is not. So channel your impostor syndrome into opening bigger doors than you had opened for you.

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  3. proslijedio/la je Tweet
    3. sij

    Ep. 6 of 'Learn Bayes Stats' is online! A thorough discussion w from about a principled workflow, the difficulties of & Bayesian foundations. See you in your favorite podcatcher or at

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  4. 31. pro 2019.

    Good argument against the folks that deny structural racism exists: it’s so big it can be quantified in the behaviour of ai that is merely exposed to it.

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  5. 21. pro 2019.

    (And yes, there’s lots in the world worth attention/complaint and this is a very minor issue on the list. But also an easy fix)

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  6. 21. pro 2019.

    Hey audio engineers, I know you think it’s fun and in-on-the-joke to use the wilhelm scream, but it’s super immersion-breaking for those of us that can recognize it. I like a clever Easter egg as much as the next person, but this one is just tired

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  7. proslijedio/la je Tweet
    Odgovor korisnicima i sljedećem broju korisnika:

    it’s only a GAM if it’s from the Gàmme region of France. Otherwise it’s sparkling splines.

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  8. 9. pro 2019.

    but I had fun implementing a pretty powerful hierarchical bivariate model that I think is useful as an intermediate step between traditional univariate analyses and the full process models (ex. diffusion, LBA, etc). 5/5

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  9. 9. pro 2019.

    I contributed mostly to the development of the attention test and Bayesian modeling (using ) thereof. There's room for even more advanced modelling, and the data/code is available for anyone to play with, ... 4/5

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  10. 9. pro 2019.

    I thought we'd maybe find that nature exposure made folks faster to respond overall, but didn't think that any more specific aspect of attention would be affected. Then the data thwarted my contrarianism, yielding posterior distributions that generally match theory preds 3/5

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  11. 9. pro 2019.

    Prior theory predicted specific aspects of attention should be affected, but existing tools to measure attention we not sufficiently nuanced to evaluate these predictions. So I developed a more rigorous test of attention and expected to have fun debunking the literature. 2/5

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  12. 9. pro 2019.

    A neat paper I co-authored was just published: It uses a tool I developed/validated for my dissertation to test how time in nature affects attention. I actually joined the research team as a skeptic, feeling that evidence from prior research was weak. 1/5

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  13. 4. pro 2019.

    An especially good xkcd today:

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  14. 29. stu 2019.

    Can't decide if this is a terrible or brilliant data viz. First impulse was that it's terrible since you can't even see two of the slices, but then I realized that's kinda the point (that universal BRCA screening costs far more than it could save)

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  15. proslijedio/la je Tweet
    21. stu 2019.

    Methods Twitter, help me out. An intro stats student is looking for an example of a 'revolutionary' paper related to stats and methods. One that wasnt cited for years but then exploded in cites OR one that was cited a lot at first, but died out.

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  16. 15. stu 2019.

    Powerful call for ongoing cultural change:

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  17. proslijedio/la je Tweet
    13. stu 2019.
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  18. proslijedio/la je Tweet

    🎺 brr brr brrrr: so excited to finally share the teaser for ’s and my podcast, Casual Inference! Let us know what you think! 🎧 🎶 featuring fun intro music 🎊 jazzy segments ☕️ casual chats about epi & stats! 🙏 sponsored by

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  19. 30. lis 2019.

    When you remove the main effects, all interactions turn into pure cross-over interactions. Does this not undermine the proposed interpretation heuristics?

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  20. 29. lis 2019.
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