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StatModeling's profile
Andrew Gelman
Andrew Gelman
Andrew Gelman
@StatModeling

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Andrew Gelman

@StatModeling

andrewgelman.com
Joined November 2010

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    Andrew Gelman‏ @StatModeling 16 Mar 2019
    • Report Tweet
    • Report NetzDG Violation

    Statistical-significance thinking is not just a bad way to publish, it’s also a bad way to think https://statmodeling.stat.columbia.edu/2019/03/16/statistical-significance-thinking-is-not-just-a-bad-way-to-publish-its-also-a-bad-way-to-think/ …

    6:19 AM - 16 Mar 2019
    • 96 Retweets
    • 341 Likes
    • Joseph M. Chirum Arisa Wiratama Jeffrey Townsend ./Jim🚴‍♂️Gruman.Rmd Bob Birgit Szabo Kelly Michaelis drcms02 Christian Oswald
    3 replies 96 retweets 341 likes
      1. New conversation
      2. Ida van Schalkwyk, Ph.D.‏ @Ida_VanS 16 Mar 2019
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        Replying to @StatModeling

        With big data we’re finding statistical significant correlations but no measurable elasticities. E.g. vehicle acceleration change of 0.0045m/s^2 for one unit change in a road environment change.

        1 reply 1 retweet 3 likes
      3. Paul Glad Mihai‏ @gladomat 17 Mar 2019
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        Replying to @Ida_VanS @StatModeling

        This is what typically happens with increasing sample size, a robust effect that is undetectable for a human, which in turn is meaningless in the greater context. Yours is a perfect example. Wish I could adjust my acceleration in those steps when biking :-).

        1 reply 0 retweets 3 likes
      4. 1 more reply
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      2. Angus B. Grieve-Smith‏ @grvsmth 16 Mar 2019
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        Replying to @StatModeling

        You agree that there's nothing wrong with testing for statistical significance, right? What you call "statistical-significance thinking" is substituting significance testing for more meaningful evaluations of the data. These researchers didn't even have a representative sample!

        1 reply 0 retweets 1 like
      3. Robert Wright III‏ @rjwthree 17 Mar 2019
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        Replying to @grvsmth @StatModeling

        I think he means it's unwise to think dichotomously. Consider: p=.03 p=.06 p=.45 Stat sig thinking would have us categorize the last two as non-signif and the first as signif, even though the first two are nearly identical. Better to think on a continuum without thresholds.

        1 reply 0 retweets 1 like
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