Intelligent Design Peer review Science

Abandoning statistical significance in science

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From Blakeley B. McShane, David Gal, Andrew Gelman, Christian Robert, Jennifer L. Tackett (22 Sep 2017) at arXiv.org:

Abandon Statistical Significance

In science publishing and many areas of research, the status quo is a lexicographic decision rule in which any result is first required to have a p-value that surpasses the 0.05 threshold and only then is consideration–often scant–given to such factors as prior and related evidence, plausibility of mechanism, study design and data quality, real world costs and benefits, novelty of finding, and other factors that vary by research domain. There have been recent proposals to change the p-value threshold, but instead we recommend abandoning the null hypothesis significance testing paradigm entirely, leaving p-values as just one of many pieces of information with no privileged role in scientific publication and decision making. We argue that this radical approach is both practical and sensible.More.

It might be easier for scientists to just be another bureaucracy.

One Reply to “Abandoning statistical significance in science

  1. 1
    johnnyb says:

    I’ve made a suggestion for how to improve beyond p-values in this video:

    https://www.youtube.com/watch?v=NkkDjL8eby0

    I haven’t had the time to write it up (I have been working on other things), but I thought I would put it out there for those interested. The actual part on p-values is late in the video, but you need to watch the first part to understand it. The part on p-values is at 24:09

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