What does “Scientists rise up against statistical significance” mean? (Comment in Nature)












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The title of the Comment in Nature Scientists rise up against statistical significance begins with:




Valentin Amrhein, Sander Greenland, Blake McShane and more than 800 signatories call for an end to hyped claims and the dismissal of possibly crucial effects.




and later contains statements like:




Again, we are not advocating a ban on P values, confidence intervals or other statistical measures — only that we should not treat them categorically. This includes dichotomization as statistically significant or not, as well as categorization based on other statistical measures such as Bayes factors.




I think I can grasp that the image below does not say that the two studies disagree because one "rules out" no effect while the other does not. But the article seems to go into much more depth that I can understand.



Towards the end there seems to be a summary in four points. Is it possible to summarize these in even simpler terms for those of us who read statistics rather than write it?




When talking about compatibility intervals, bear in mind four things.




  • First, just because the interval gives the values most compatible with the data, given the assumptions, it doesn’t mean values outside it are incompatible; they are just less compatible...


  • Second, not all values inside are equally compatible with the data, given the assumptions...


  • Third, like the 0.05 threshold from which it came, the default 95% used to compute intervals is itself an arbitrary convention...


  • Last, and most important of all, be humble: compatibility assessments hinge on the correctness of the statistical assumptions used to compute the interval...






Nature: Scientists rise up against statistical significance






statistical-significance p-value bias







share|cite|improve this question









$endgroup$

















    3












    $begingroup$


    The title of the Comment in Nature Scientists rise up against statistical significance begins with:




    Valentin Amrhein, Sander Greenland, Blake McShane and more than 800 signatories call for an end to hyped claims and the dismissal of possibly crucial effects.




    and later contains statements like:




    Again, we are not advocating a ban on P values, confidence intervals or other statistical measures — only that we should not treat them categorically. This includes dichotomization as statistically significant or not, as well as categorization based on other statistical measures such as Bayes factors.




    I think I can grasp that the image below does not say that the two studies disagree because one "rules out" no effect while the other does not. But the article seems to go into much more depth that I can understand.



    Towards the end there seems to be a summary in four points. Is it possible to summarize these in even simpler terms for those of us who read statistics rather than write it?




    When talking about compatibility intervals, bear in mind four things.




    • First, just because the interval gives the values most compatible with the data, given the assumptions, it doesn’t mean values outside it are incompatible; they are just less compatible...


    • Second, not all values inside are equally compatible with the data, given the assumptions...


    • Third, like the 0.05 threshold from which it came, the default 95% used to compute intervals is itself an arbitrary convention...


    • Last, and most important of all, be humble: compatibility assessments hinge on the correctness of the statistical assumptions used to compute the interval...






    Nature: Scientists rise up against statistical significance






    statistical-significance p-value bias







    share|cite|improve this question









    $endgroup$















      3












      3








      3





      $begingroup$


      The title of the Comment in Nature Scientists rise up against statistical significance begins with:




      Valentin Amrhein, Sander Greenland, Blake McShane and more than 800 signatories call for an end to hyped claims and the dismissal of possibly crucial effects.




      and later contains statements like:




      Again, we are not advocating a ban on P values, confidence intervals or other statistical measures — only that we should not treat them categorically. This includes dichotomization as statistically significant or not, as well as categorization based on other statistical measures such as Bayes factors.




      I think I can grasp that the image below does not say that the two studies disagree because one "rules out" no effect while the other does not. But the article seems to go into much more depth that I can understand.



      Towards the end there seems to be a summary in four points. Is it possible to summarize these in even simpler terms for those of us who read statistics rather than write it?




      When talking about compatibility intervals, bear in mind four things.




      • First, just because the interval gives the values most compatible with the data, given the assumptions, it doesn’t mean values outside it are incompatible; they are just less compatible...


      • Second, not all values inside are equally compatible with the data, given the assumptions...


      • Third, like the 0.05 threshold from which it came, the default 95% used to compute intervals is itself an arbitrary convention...


      • Last, and most important of all, be humble: compatibility assessments hinge on the correctness of the statistical assumptions used to compute the interval...






      Nature: Scientists rise up against statistical significance






      statistical-significance p-value bias







      share|cite|improve this question









      $endgroup$




      The title of the Comment in Nature Scientists rise up against statistical significance begins with:




      Valentin Amrhein, Sander Greenland, Blake McShane and more than 800 signatories call for an end to hyped claims and the dismissal of possibly crucial effects.




      and later contains statements like:




      Again, we are not advocating a ban on P values, confidence intervals or other statistical measures — only that we should not treat them categorically. This includes dichotomization as statistically significant or not, as well as categorization based on other statistical measures such as Bayes factors.




      I think I can grasp that the image below does not say that the two studies disagree because one "rules out" no effect while the other does not. But the article seems to go into much more depth that I can understand.



      Towards the end there seems to be a summary in four points. Is it possible to summarize these in even simpler terms for those of us who read statistics rather than write it?




      When talking about compatibility intervals, bear in mind four things.




      • First, just because the interval gives the values most compatible with the data, given the assumptions, it doesn’t mean values outside it are incompatible; they are just less compatible...


      • Second, not all values inside are equally compatible with the data, given the assumptions...


      • Third, like the 0.05 threshold from which it came, the default 95% used to compute intervals is itself an arbitrary convention...


      • Last, and most important of all, be humble: compatibility assessments hinge on the correctness of the statistical assumptions used to compute the interval...






      Nature: Scientists rise up against statistical significance






      statistical-significance p-value bias




      statistical-significance p-value bias






      share|cite|improve this question













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      uhohuhoh

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          I'll try.




          1. The confidence interval (which they rename compatibility interval) shows the values of the parameter that are most compatible with the data. But that doesn't mean the values outside the interval are absolutely incompatible with the data.

          2. Values near the middle of the confidence (compatibili5y) interval are more compatible with the data than values near the ends of the interval.

          3. 95% is just a convention. You can compute 90% or 99% or any% intervals.

          4. The confidence/compatibility intervals are only helpful if the experiment was done properly, if the analysis was done according to a preset plan, and the data conform with the assumption of the analysis methods. If you've got bad data analyzed badly, the compatibility interval is not meaningful or helpful.






          share|cite|improve this answer









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            active

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            $begingroup$

            I'll try.




            1. The confidence interval (which they rename compatibility interval) shows the values of the parameter that are most compatible with the data. But that doesn't mean the values outside the interval are absolutely incompatible with the data.

            2. Values near the middle of the confidence (compatibili5y) interval are more compatible with the data than values near the ends of the interval.

            3. 95% is just a convention. You can compute 90% or 99% or any% intervals.

            4. The confidence/compatibility intervals are only helpful if the experiment was done properly, if the analysis was done according to a preset plan, and the data conform with the assumption of the analysis methods. If you've got bad data analyzed badly, the compatibility interval is not meaningful or helpful.






            share|cite|improve this answer









            $endgroup$


















              4












              $begingroup$

              I'll try.




              1. The confidence interval (which they rename compatibility interval) shows the values of the parameter that are most compatible with the data. But that doesn't mean the values outside the interval are absolutely incompatible with the data.

              2. Values near the middle of the confidence (compatibili5y) interval are more compatible with the data than values near the ends of the interval.

              3. 95% is just a convention. You can compute 90% or 99% or any% intervals.

              4. The confidence/compatibility intervals are only helpful if the experiment was done properly, if the analysis was done according to a preset plan, and the data conform with the assumption of the analysis methods. If you've got bad data analyzed badly, the compatibility interval is not meaningful or helpful.






              share|cite|improve this answer









              $endgroup$
















                4












                4








                4





                $begingroup$

                I'll try.




                1. The confidence interval (which they rename compatibility interval) shows the values of the parameter that are most compatible with the data. But that doesn't mean the values outside the interval are absolutely incompatible with the data.

                2. Values near the middle of the confidence (compatibili5y) interval are more compatible with the data than values near the ends of the interval.

                3. 95% is just a convention. You can compute 90% or 99% or any% intervals.

                4. The confidence/compatibility intervals are only helpful if the experiment was done properly, if the analysis was done according to a preset plan, and the data conform with the assumption of the analysis methods. If you've got bad data analyzed badly, the compatibility interval is not meaningful or helpful.






                share|cite|improve this answer









                $endgroup$



                I'll try.




                1. The confidence interval (which they rename compatibility interval) shows the values of the parameter that are most compatible with the data. But that doesn't mean the values outside the interval are absolutely incompatible with the data.

                2. Values near the middle of the confidence (compatibili5y) interval are more compatible with the data than values near the ends of the interval.

                3. 95% is just a convention. You can compute 90% or 99% or any% intervals.

                4. The confidence/compatibility intervals are only helpful if the experiment was done properly, if the analysis was done according to a preset plan, and the data conform with the assumption of the analysis methods. If you've got bad data analyzed badly, the compatibility interval is not meaningful or helpful.







                share|cite|improve this answer












                share|cite|improve this answer



                share|cite|improve this answer










                answered 51 mins ago









                Harvey MotulskyHarvey Motulsky

                10.9k44485




                10.9k44485






























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