vbid/9789819777488

Description

This book explains the importance of using the probability that the hypothesis is correct (PHC), an intuitive measure that anyone can understand, as an alternative to the�p-value. In order to overcome the �reproducibility crisis� caused by the misuse of significance tests, this book provides a detailed explanation of the mechanism of�p-hacking using significance tests, and concretely shows the merits of PHC as an alternative to�p-values. In March 2019, two impactful papers on statistics were published. One paper, “Moving to a World Beyond �p�< 0.05��, was featured in the scholarly journal�The American Statistician, overseen by the American Statistical Association. The title of the first chapter is �Don't Say �Statistically Significant��, and it uses the imperative form to clearly forbid the use of significance testing. Another paper, �Retire statistical significance�, was published in the prestigious scientific journal�Nature. This commentary was endorsed by more than 800 scientists, advocating for the statement, �We agree, and call for the entire concept of statistical significance to be abandoned.� Consider a study comparing the duration of hospital stays between treatments A and B. Previously, research conclusions were typically stated as: �There was a statistically significant difference at the 5% level in the average duration of hospital stays.� This phrasing is quite abstract. Instead, we present the following conclusion as an example: (1) The average duration of hospital stays for Group A is at least half a day shorter than for Group B. (2) 71% of patients in Group A have shorter hospital stays than the average for Group B. (3) Group A has an average hospital stay that is, on average, no more than 94% of that of Group B. Then, the probability that the expression is correct is shown. That is the PHC curve.Typham this is the title: Statistical Significance and the PHC Curve