Significance Testing in Theory and Practice
by Daniel Greco
published in The British Journal for the Philosophy of Science, 2011
Frequentism and Bayesianism represent very different approaches to hypothesis testing, and this presents a skeptical... more Frequentism and Bayesianism represent very different approaches to hypothesis testing, and this presents a skeptical challenge for Bayesians. Given that most empirical research uses frequentist methods, why (if at all) should we rely on it? While it is well known that there are conditions under which Bayesian and frequentist methods agree, without some reason to think these conditions are typically met, the Bayesian hasn’t shown why we are usually safe in relying on results reported by significance testers. In this article, I provide arguments that such conditions will usually be met; the Bayesian can maintain her theoretical disagreement with the frequentist while holding that her error is mostly harmless in practice.
Bayesian and frequentist models: legitimate choices for different purposes of clinical research
Journal of Evaluation in Clinical Practice
Volume 16, Issue 6, pages 1045–1047, December 2010
Objective Bayesian and frequentist approaches to statistical modelling in epidemiology are often pitted against each... more
Objective Bayesian and frequentist approaches to statistical modelling in epidemiology are often pitted against each other as if they represented diametrically opposing philosophies. However, both approaches have a role to play in clinical epidemiology and the evaluation of clinical practice.
Methods Here I present an overview of the philosophical underpinnings of the Bayesian and frequentist approaches, showing that each model has its place depending on the philosophical and evaluative needs of the user.
Results If the user's approach to a clinical problem places an emphasis on identifying causal relationships, a frequentist approach might be best suited. On the other hand, if the user takes an approach in which estimating a priori probabilities is appropriate, a Bayesian approach might be more appropriate. One could imagine both approaches used for the same study.
Conclusions Bayesian and frequentist approaches are complementary tools in the clinical evaluator's toolkit.