A B for every p
Posted: Thu Jun 20, 2019 12:27 pm
Hi,
A colleague gave me this article https://royalsocietypublishing.org/doi/ ... .2019.0174 about the limitations of the p-value. It's a review of 4 methods that anyone can use to overcome those limitations and get a better understanding of the data.
The authors talk, among other things, about a simplified Baye Factor called the Bayes factor upper bound, a way of getting a way more simple interpretation of the p-value.
Jamovi has a module about Bayesian stats, Jasp-style, but I was wondering if it could be interesting to give the user the possibility to tick a case allowing the computing of this Bayes factor upper bound. Like a first taste of the Bayesian world available for the p-value maniacs all over the world (and for humans too). Or compute it by default, to get à B for every p, like the author of the article said.
What do the stats wizards think about this approach?
Have a nice day.
A colleague gave me this article https://royalsocietypublishing.org/doi/ ... .2019.0174 about the limitations of the p-value. It's a review of 4 methods that anyone can use to overcome those limitations and get a better understanding of the data.
The authors talk, among other things, about a simplified Baye Factor called the Bayes factor upper bound, a way of getting a way more simple interpretation of the p-value.
Jamovi has a module about Bayesian stats, Jasp-style, but I was wondering if it could be interesting to give the user the possibility to tick a case allowing the computing of this Bayes factor upper bound. Like a first taste of the Bayesian world available for the p-value maniacs all over the world (and for humans too). Or compute it by default, to get à B for every p, like the author of the article said.
What do the stats wizards think about this approach?
Have a nice day.
