Reproducibility in Biomedical Research: Epistemological and Statistical Problems, 2nd Ed. explores the ideas and conundrums inherent in scientific research.
Reproducibility is one of the biggest challenges in biomedical research. It affects not only the ability to replicate results, but the very trust in the findings. Since published in 2019, Reproducibility of Biomedical Research: Epistemological and Statistical Problems established itself as a solid ethical reference in the area, leading to significant reflection on biomedical research. The second edition addresses new challenges to reproducibility in biosciences, namely reproducibility of machine learning Artificial Intelligence (AI), reproducibility of translation from research to medical care, and the fundamental challenges to reproducibility. All current chapters will be expanded to cover advances in the topics previously addressed.
Reproducibility in Biomedical Research: Epistemological and Statistical Problems, 2nd Ed. provides biomedical researchers with a framework to better understand the reproducibility challenges in the area. Newly introduced interactive exercises and updated case studies help students understand the fundamental concepts involved in the area.
1. Chapter 1 : Introduction - p. Page xxviii 2. Chapter 2 : The problem of irreproducibility - p. Page 27 3. Chapter 3 : Validity of biomedical science, reproducibility, and irreproducibility - p. Page 41 4. Chapter 4 : The logic of certainty versus the logic of discovery - p. Page 54 5. Chapter 5 : The logic of probability and statistics - p. Page 84 6. Chapter 6 : Causation, process metaphor, and reductionism - p. Page 115 7. Chapter 7 : Case studies in clinical biomedical research - p. Page 137 8. Chapter 8 : Case studies in basic biomedical research - p. Page 156 9. Chapter 9 : Case studies in computational biomedical research - p. Page 170 10. Chapter 10 : Case studies in translational research - p. Page 192 11. Chapter 11 : Case studies in machine learning artificial intelligence - p. Page 226 12. Chapter 12 : Chaotic and Complex systems, statistics, and far-from-equilibrium thermodynamics - p. Page 264 13. Chapter 13 : The fundamental problem - p. Page 293 14. Chapter 14 : Epilogue - p. Page 315 15. Appendix A : An introduction to the logic of logic - p. Page 330 16. Appendix B : Introduction to the logic of probability and statistics - p. Page 342 17. Appendix C : Moving away from sample-based analyses for translational research - p. Page 366 18. Glossary - p. Page 384 19. GENERIC-TITLE - p. Page 384 20. Index - p. Page 384
Reproducibility in Biomedical Research: Epistemological and Statistical Problems, 2nd Ed. explores the ideas and conundrums inherent in scientific research.
Reproducibility is one of the biggest challenges in biomedical research. It affects not only the ability to replicate results, but the very trust in the findings. Since published in 2019, Reproducibility of Biomedical Research: Epistemological and Statistical Problems established itself as a solid ethical reference in the area, leading to significant reflection on biomedical research. The second edition addresses new challenges to reproducibility in biosciences, namely reproducibility of machine learning Artificial Intelligence (AI), reproducibility of translation from research to medical care, and the fundamental challenges to reproducibility. All current chapters will be expanded to cover advances in the topics previously addressed.
Reproducibility in Biomedical Research: Epistemological and Statistical Problems, 2nd Ed. provides biomedical researchers with a framework to better understand the reproducibility challenges in the area. Newly introduced interactive exercises and updated case studies help students understand the fundamental concepts involved in the area.
1. Chapter 1 : Introduction - p. Page xxviii 2. Chapter 2 : The problem of irreproducibility - p. Page 27 3. Chapter 3 : Validity of biomedical science, reproducibility, and irreproducibility - p. Page 41 4. Chapter 4 : The logic of certainty versus the logic of discovery - p. Page 54 5. Chapter 5 : The logic of probability and statistics - p. Page 84 6. Chapter 6 : Causation, process metaphor, and reductionism - p. Page 115 7. Chapter 7 : Case studies in clinical biomedical research - p. Page 137 8. Chapter 8 : Case studies in basic biomedical research - p. Page 156 9. Chapter 9 : Case studies in computational biomedical research - p. Page 170 10. Chapter 10 : Case studies in translational research - p. Page 192 11. Chapter 11 : Case studies in machine learning artificial intelligence - p. Page 226 12. Chapter 12 : Chaotic and Complex systems, statistics, and far-from-equilibrium thermodynamics - p. Page 264 13. Chapter 13 : The fundamental problem - p. Page 293 14. Chapter 14 : Epilogue - p. Page 315 15. Appendix A : An introduction to the logic of logic - p. Page 330 16. Appendix B : Introduction to the logic of probability and statistics - p. Page 342 17. Appendix C : Moving away from sample-based analyses for translational research - p. Page 366 18. Glossary - p. Page 384 19. GENERIC-TITLE - p. Page 384 20. Index - p. Page 384
