بازتولیدپذیری در تحقیقات زیست‌پزشکی: مسائل معرفت‌شناختی و آماری و آینده ۲۰۲۴
Reproducibility in Biomedical Research: Epistemological and Statistical Problems and the Future 2024

Front Cover -- Reproducibility in Biomedical Research -- Copyright Page -- Dedication -- Quotes -- Contents -- Preface to the second edition -- Looking just over the horizon -- Machine learning artificial intelligence -- Translational research -- Biological realism and Chaos and Complexity -- Probability and statistical epistemology -- Randomness as fundamental and foundational -- Finally... -- Preface to the first edition -- 1 Introduction -- Productive irreproducibility -- The multifaceted notion of reproducibility and irreproducibility -- Beyond "stamp collecting" -- Replication reproducibility -- Within-experiment reproducibility -- Narrow, broad, conceptual, and translational reproducibility -- Turning from the past with an eye to the future -- Machine learning artificial intelligence -- A challenge to clinical research posed by a new notion of translational research -- Toward more realistic experimental designs and analyses -- The fundamental causes of unproductive irreproducibility -- Illogic -- The paradox and dilemma of the "population" -- The logic of population and sample -- The proof of the pudding, in this case sample/population biomedical research -- Wrestling with the fundamental problem -- Proceeding from what is certain but not useful to what is uncertain but useful -- Precision versus accuracy -- Precision and statistical distributions -- Combinatorics -- Dynamics -- Machine learning artificial intelligence and the emperor's new clothes -- Knowledge is prediction, prediction is reproducibility or productive irreproducibility -- The ascendancy of experimental science -- Challenges to prediction and thus biomedical research -- Predicting to the unknowable population -- Hume's Problem of Induction -- When traditional experimental design and statistics breed unproductive irreproducibility -- Data do not and cannot speak for themselves.