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


References containing arguments against null hypothesis significance testing?Statistical significance for correlated significance testsDoes Greenhouse-Geisser correction influence the effect size estimation, the statistical significance treshold, or both of them?Is the “hybrid” between Fisher and Neyman-Pearson approaches to statistical testing really an “incoherent mishmash”?A psychology journal banned p-values and confidence intervals; is it indeed wise to stop using them?Statistical significanceStatistical test significanceASA discusses limitations of $p$-values - what are the alternatives?Is this the solution to the p-value problem?Statistical significance between years; what test to use













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










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










    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










      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






      share|cite|improve this question













      share|cite|improve this question











      share|cite|improve this question




      share|cite|improve this question










      asked 1 hour ago









      uhohuhoh

      1264




      1264




















          1 Answer
          1






          active

          oldest

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          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









          $endgroup$












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            active

            oldest

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            active

            oldest

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            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












              $begingroup$

              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 49 mins ago









                Harvey MotulskyHarvey Motulsky

                10.9k44485




                10.9k44485



























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                    ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result