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It is possible to obtain a large Bayes Factor (BF) favoring the null hypothesis when both the null and alternative hypotheses have low likelihoods, and there are other hypotheses being ignored that are much more strongly supported by the data. As sample sizes become large it becomes increasingly probable that a strong BF favouring a point null against a conventional Bayesian vague alternative co-occurs with a BF favouring various specific alternatives against the null. For any BF threshold q and sample mean, there is a value n such that sample sizes larger than n guarantee that although the BF comparing H0 against a conventional (vague) alternative exceeds q, nevertheless for some range of hypothetical {mu}, a BF comparing H0 against {mu} in that range falls below 1/q. This paper discusses the conditions under which this conundrum occurs and investigates methods for resolving it.
There has been strong recent interest in testing interval null hypothesis for improved scientific inference. For example, Lakens et al (2018) and Lakens and Harms (2017) use this approach to study if there is a pre-specified meaningful treatment effe
Timing channels are a significant and growing security threat in computer systems, with no established solution. We have recently argued that the OS must provide time protection, in analogy to the established memory protection, to protect application
Randomization (a.k.a. permutation) inference is typically interpreted as testing Fishers sharp null hypothesis that all effects are exactly zero. This hypothesis is often criticized as uninteresting and implausible. We show, however, that many random
Global null testing is a classical problem going back about a century to Fishers and Stouffers combination tests. In this work, we present simple martingale analogs of these classical tests, which are applicable in two distinct settings: (a) the onli
A simple Bayesian approach to nonparametric regression is described using fuzzy sets and membership functions. Membership functions are interpreted as likelihood functions for the unknown regression function, so that with the help of a reference prio