As a general comment, this is why Bayesian statistics is much more amenable to knowledge-generation than frequentist statistics. The statement “the hyperactivity increase in the experimental group was 0.36+/-2.00, and that range solidly includes 0” (with the variance of that estimate pulled out of thin air) is much more meaningful than “we can’t be sure it’s not zero.”
I agree with the second sentence, and the first might be true, but the second isn’t evidence for the first; interval estimation vs. hypothesis testing is an independent issue to Bayesianism vs. frequentism. There are Bayesian hypothesis tests and frequentist interval estimates.
Agreed that both have those tools, and rereading my comment I think “approach” may have been a more precise word than “statistics.” If you think in terms of “my results are certain, reality is uncertain” then the first tool you reach for is “let’s make an interval estimate / put a distribution on reality,” whereas if you think in terms of “reality is certain, my results are uncertain” then the first tool you reach for is hypothesis testing. Such defaults have very important effects on what actually gets used in studies.
I agree with the second sentence, and the first might be true, but the second isn’t evidence for the first; interval estimation vs. hypothesis testing is an independent issue to Bayesianism vs. frequentism. There are Bayesian hypothesis tests and frequentist interval estimates.
Agreed that both have those tools, and rereading my comment I think “approach” may have been a more precise word than “statistics.” If you think in terms of “my results are certain, reality is uncertain” then the first tool you reach for is “let’s make an interval estimate / put a distribution on reality,” whereas if you think in terms of “reality is certain, my results are uncertain” then the first tool you reach for is hypothesis testing. Such defaults have very important effects on what actually gets used in studies.