I think the author is treating the frequentists somewhat unfairly. A good design involves both NHST and equivalence tests + discussion about power/effect size (

I think the author is treating the frequentists somewhat unfairly. A good design involves both NHST and equivalence tests + discussion about power/effect size (imo). Random grab from my methods collection in my literature tool: "A Comparison Of Equivalence Testing In Combination With Hypothesis Testing And Effect Sizes": http://digitalcommons.wayne.edu/jmasm/vol2/iss2/6/ http://digitalcommons.wayne.edu/jmasm/vol2/iss2/6/ I think the biggest problem is that many fields just churn out people who can click buttons in SPSS but couldn't really tell you what a p-value actually represents. "Unfortunately" it's fairly easy to do the testing with tools. From students, I often get the feeling that tests are conducted for the sake of conduction a test (talking about BA theses etc.). I'm not sure how wide spread Bayesian thinking is in other fields. Coming from CS it is somewhat natural since it's pretty pervasive in AI/ML. I think our econ students didn't get exposed to it at all (iirc). Would love some feedback from other fields :)