I think the author is treating the frequentists somewhat unfairly. A good design involves both NHST and equivalence tests + discussion about power&#x2F;effect size (imo).<p>Random grab from my methods collection in my literature tool: &quot;A Comparison Of Equivalence Testing In Combination With Hypothesis Testing And Effect Sizes&quot;: <a href="http:&#x2F;&#x2F;digitalcommons.wayne.edu&#x2F;jmasm&#x2F;vol2&#x2F;iss2&#x2F;6&#x2F;" rel="nofollow">http:&#x2F;&#x2F;digitalcommons.wayne.edu&#x2F;jmasm&#x2F;vol2&#x2F;iss2&#x2F;6&#x2F;</a><p>I think the biggest problem is that many fields just churn out people who can click buttons in SPSS but couldn&#x27;t really tell you what a p-value actually represents. &quot;Unfortunately&quot; it&#x27;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.).<p>I&#x27;m not sure how wide spread Bayesian thinking is in other fields. Coming from CS it is somewhat natural since it&#x27;s pretty pervasive in AI&#x2F;ML.
I think our econ students didn&#x27;t get exposed to it at all (iirc). Would love some feedback from other fields :)