One more time on Bayes, Popper, and Kuhn

There was a lot of fascinating discussion on this entry from a few days ago. I feel privileged to be able to get feedback from scientists with different perspectives than my own. Anyway, I’d like to comment on some things that Dan Navarro wrote in this discussion. Not to pick on Dan but because I think his comments, and my responses, may highlight some different views about what is meant by “Bayesian inference” (or, as I would prefer to say, “Bayesian data analysis,” to include model building and model checking as well as inference).

So here goes . . . Continue reading

“If you do not know what you would have done under all possible scenarios, then you cannot know the Type I error rate for your analysis.”

José Iparraguirre writes: Just finished “Understanding Statistics and Experimental Design. How to Not Lie with Statistics”, by Michael­ Herzog, Gregory­ Francis, and Aaron ­Clarke (Springer, 2019). Near the end (p. 128), I read the following regarding “optional stopping”: …suppose a … Continue reading

Empirical implications of Empirical Implications of Theoretical Models

Robert Bloomfield writes: Most of the people in my field (accounting, which is basically applied economics and finance, leavened with psychology and organizational behavior) use ‘positive research methods’, which are typically described as coming to the data with a predefined … Continue reading