• jeffw@lemmy.world
    27·
    10 hours ago

    It’s called publication bias, idiots. Research that has no strong result doesn’t get published

    • ethaver@kbin.earth
      8·
      9 hours ago

      which is such a shame because there really should be more evidence for what is and isn’t placebos

      • udon@lemmy.world
        4·
        7 hours ago

        Easier said than done though. If the results are non-significant, that can be due to all sorts of things only one of them being a lack of an actual effect. If your measure is bad/noisy/not well calibrated, your research plan has flaws etc., the sample is too small, … Most non-significant results are due to bad research and it’s hard to identify the other ones. Preregistration and registered reports are some ideas to change that

  • Kairos@lemmy.today
    9·
    10 hours ago

    Z score for what? What are these numbers.

    I know what a Z score is I just don’t know what this means.

    • Avicenna@lemmy.world
      3·
      4 hours ago

      I came here with the same question but now I realize that if I ask it I will only get replies explaining me what Z-score is and not Z-score of what. So I will just assume it is sth akin to h-index. Still does not make much sense to me as to why average h-index papers “don’t survive” (i.e get rejected because no one is interested lets say) where as negative ones do.

    • marcos@lemmy.world
      5·
      8 hours ago

      As I understand it, the data there is the histogram of z-value observed by some census of published papers.

      They should make a normal curve, but the publishing process is biased. (On the best case, otherwise the research process would be biased.)

      • Squirrelsdrivemenuts@lemmy.world
        2·
        4 hours ago

        But we also prioritize research where we suspect/hypothesize differences, so I think even if all research was published it wouldn’t necessarily be a normal distribution.

    • TropicalDingdong@lemmy.world
      9·
      10 hours ago

      Z value (also known as z-score) is the distance (signed) between your model and a prediction.

      If your model is a mean (the average), the z-scores are the set of differences between the mean and the values used to compose the mean.

      If your model is a regression (relating, say, two variables relating x and y), then the z-score is the difference between the regression line and the values used to fit the regression.