An important step when designing an empirical study is to justify the sample size that will be
collected. The key aim of a sample size justification for such studies is to explain how the …

This is an up-to-date introduction to and overview of the Minimum Description Length (MDL)
Principle, a theory of inductive inference that can be applied to general problems in …

Safe anytime-valid inference (SAVI) provides measures of statistical evidence and certainty—
e-processes for testing and confidence sequences for estimation—that remain valid at all …

Time-uniform, nonparametric, nonasymptotic confidence sequences Page 1 The Annals of
Statistics 2021, Vol. 49, No. 2, 1055–1080 https://doi.org/10.1214/20-AOS1991 © Institute of …

We derive confidence intervals (CIs) and confidence sequences (CSs) for the classical
problem of estimating a bounded mean. Our approach generalizes and improves on the …

The most widely used concept of statistical inference—the p-value—is too complicated for
effective communication to a wide audience. This paper introduces a simpler way of …

We present a unified framework for deriving PAC-Bayesian generalization bounds. Unlike
most previous literature on this topic, our bounds are anytime-valid (ie, time-uniform) …

We propose a general method for constructing confidence sets and hypothesis tests that
have finite-sample guarantees without regularity conditions. We refer to such procedures as …

E-values: Calibration, combination and applications Page 1 The Annals of Statistics 2021, Vol.
49, No. 3, 1736–1754 https://doi.org/10.1214/20-AOS2020 © Institute of Mathematical Statistics …

E-statistics, group invariance and anytime-valid testing Page 1 The Annals of Statistics 2024,
Vol. 52, No. 4, 1410–1432 https://doi.org/10.1214/24-AOS2394 © Institute of Mathematical …