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  • Since: 2024-05-10
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    統計学


    01 Inferential statistics as descriptive statistics: There is no replication crisis if we don't expect replication


    02 Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise


    03 Discuss practical importance of results based on interval estimates and p-value functions, not only on point estimates and null p-values


    04 Selecting on statistical significance and practical importance is wrong


    05 Scientists rise up against statistical significance - 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


    06 Advancing statistics reform: Ways to improve thinking and practice in the face of resistance


    07 Statistical concepts in the relation to reality


    08 Language for communicating frequentist results about treatment effects


    09 Philosophy and the practice of Bayesian statistics


    10 Living with p values: resurrecting a Bayesian perspective on frequentist statistics


    11 Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations


    12 Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations


    13 Are confidence intervals better termed “uncertainty intervals”?


    14 To Aid Scientific Inference, Emphasize Unconditional Compatibility Descriptions of Statistics


    15 Chow and Greenland: “Unconditional Interpretations of Statistics”


    16 Principles and guidelines for applied statistics


    17 The most compatible value with the data within a confidence interval - Sander Greenland talks about the philosophy of statistics


    18 Multiple comparisons controversies are about context and costs, not frequentism versus Bayesianism


    19 Principles and guidelines for applied statistics Posts 1 and 2

    因果推論


    401 On a Class of Bias-Amplifying Variables that Endanger Effect Estimates


    402 Individual response


    その他


    501 They Studied Dishonesty. Was Their Work a Lie? - Dan Ariely and Francesca Gino became famous for their research into why we bend the truth. Now they’ve both been accused of fabricating data.


    502 What’s the story behind that paper by the Center for Open Science team that just got retracted?