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Hands-on tutorial on e-values, safe tests and anytime-valid confidence intervals for efficient statistical inference

Prof. Peter Grünwald
CWI Amsterdam/Leiden University ~ Machine Learning/Mathematical Institute
Dr. Rianne de Heide
VU Amsterdam ~ Mathematics
Dr. Udo Boehm
Dr. Rosanne J Turner
CWI Amsterdam/University Medical Center Utrecht ~ Machine Learning/Brain Center
Alexander Ly
CWI Amsterdam ~ Machine Learning

Recently developed safe tests based on e-values, and anytime-valid confidence intervals form a suite of statistical methods that simplify and optimise the design, conduct, and inferential process for both single-lab experiments and large-scale multi-lab (replication) studies. Safe tests combine the interpretability of Bayes factors (i.e. measuring evidence for and against a null hypothesis) with frequentist power and type I error guarantees. These guarantees are maintained even if the safe test is conducted after each observation and used to determine whether the experiment should be (prematurely) stopped or continued. Similarly, unlike 95% (Bayesian) credible intervals and 95% (frequentist) confidence intervals, a 95% anytime-valid confidence interval will, with at least 95% chance, cover the true effect size regardless of whether or how data collection is stopped. In this workshop we will provide an introduction to this novel framework of statistical inference, and show how it can be exploited to yield more generalisable conclusions with less data. We will alternate between short theoretical lectures and hands-on practical sessions that focus on designing and making inference for practical problems with R/RStudio.



Adaptive design of experiments
large-scale multi-lab (replication) studies
optimal design of experiments

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Cite this as:

Grünwald, P. D., de Heide, R., Boehm, U., Turner, R. J., & Ly, A. (2023, July). Hands-on tutorial on e-values, safe tests and anytime-valid confidence intervals for efficient statistical inference. Abstract published at MathPsych/ICCM/EMPG 2023. Via