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Interpreting Biomedical Science
Experiment, Evidence, and Belief
- 1st Edition - June 11, 2015
- Author: Ülo Maiväli
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 4 1 8 6 8 9 - 7
- eBook ISBN:9 7 8 - 0 - 1 2 - 4 1 9 9 5 6 - 9
Interpreting Biomedical Science: Experiment, Evidence, and Belief discusses what can go wrong in biological science, providing an unbiased view and cohesive understan… Read more
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Request a sales quoteInterpreting Biomedical Science: Experiment, Evidence, and Belief
discusses what can go wrong in biological science, providing an unbiased view and cohesive understanding of scientific methods, statistics, data interpretation, and scientific ethics that are illustrated with practical examples and real-life applications.Casting a wide net, the reader is exposed to scientific problems and solutions through informed perspectives from history, philosophy, sociology, and the social psychology of science.
The book shows the differences and similarities between disciplines and different eras and illustrates the concept that while sound methodology is necessary for the progress of science, we cannot succeed without a right culture of doing things.
- Features theoretical concepts accompanied by examples from biological literature
- Contains an introduction to various methods, with an emphasis on statistical hypothesis testing
- Presents a clear argument that ties the motivations and ethics of individual scientists to the success of their science
- Provides recommendations on how to safeguard against scientific misconduct, fraud, and retractions
- Arms young scientists with practical knowledge that they can use every day
researchers in life and biomedical sciences, postdocs, and students in scientific methodology classes
- Preface
- This I Believe in Science
- Acknowledgments
- Introduction
- Science Made Easy
- Did the Greeks Get their Math Right but their Science Wrong?
- The Scientific Revolution
- Deduction and Induction as Two Approaches to Scientific Inference
- References
- Part I: What Is at Stake: The Skeptical Argument
- Chapter 1. Do We Need a Science of Science?
- 1.1 Are We Living in the Golden Age of Science?
- 1.2 R&D and the Cost of Medicine
- 1.3 The Efficiency of Drug Discovery
- 1.4 Factors that Endanger the Quality of Medical Evidence
- 1.5 The Stability of Evidence-Based Medical Practices
- 1.6 Reproducibility of Basic Biomedical Science
- 1.7 Is Reproducibility a Good Criterion of Quality of Research?
- 1.8 Is Biomedical Science Self-Correcting?
- 1.9 Do We Need a Science of Science?
- References
- Chapter 2. The Basis of Knowledge: Causality and Truth
- 2.1 Scientific Realism and Truth
- 2.2 Hume’s Gambit
- 2.3 Kant’s Solution
- 2.4 Why Induction Is Poor Deduction
- 2.5 Popper’s Solution
- 2.6 Why Deduction Is Poor Induction
- 2.7 Does Lung Cancer Cause Smoking?
- 2.8 Correlation, Concordance, and Regression
- 2.9 From Correlation to Causation
- 2.10 From Experiment to Causation
- 2.11 Is Causality a Scientific Concept?
- References
- Chapter 1. Do We Need a Science of Science?
- Part II: The Method
- Chapter 3. Study Design
- 3.1 Why Do Experiments?
- 3.2 Population and Sample
- 3.3 Regression to the Mean
- 3.4 Why Repeat an Experiment?
- 3.5 Technical Versus Biological Replication of Experiments
- 3.6 Experimental Controls
- 3.7 Multiplicities
- 3.8 Conclusion: How to Design an Experiment
- References
- Chapter 4. Data and Evidence
- 4.1 Looking at Data
- 4.2 Modeling Data
- 4.3 What Is Probability?
- 4.4 Assumptions Behind Frequentist Statistical Tests
- 4.5 The Null Hypothesis
- 4.6 The P value
- 4.7 Neyman-Pearson Hypothesis Testing
- 4.8 Multiple Testing in the Context of NPHT
- 4.9 P Value as a Measure of Evidence
- 4.10 The “Error Bars”
- 4.11 Likelihood as an Unbiased Measure of Evidence
- 4.12 Conclusion: Ideologies Behind Some Methods of Statistical Inference
- References
- Chapter 5. Truth and Belief
- 5.1 From Long-Run Error Probabilities to Degrees of Belief
- 5.2 Bayes Theorem: What Makes a Rational Being?
- 5.3 Testing in the Infinite Hypothesis Space: Bayesian Parameter Estimation
- 5.4 All Against All: Bayesianism Versus Frequentism Versus Likelihoodism
- 5.5 Bayesianism as a Philosophy
- 5.6 Bayesianism and the Progress of Science
- 5.7 Conclusion to Part II
- References
- Chapter 3. Study Design
- Part III: The Big Picture
- Chapter 6. Interpretation
- 6.1 Hypothesis Testing at Small Samples
- 6.2 Is Intuitive Reasoning Bayesian?
- 6.3 The Molecular Biology Lab as Research Subject
- 6.4 How to Win Fame and Influence People
- References
- Chapter 7. Science as a Social Enterprise
- 7.1 The Revolutionary Road of Thomas Kuhn
- 7.2 The Anarchism of Paul Feyerabend
- 7.3 The Communism of Robert K. Merton
- 7.4 Science as an Oligogracy
- 7.5 Tragedy of the Proxy
- 7.6 Science as a Lottery
- 7.7 Science as a Career
- References
- Chapter 8. What Can Be Done: A Utopia
- 8.1 Take Methodology Seriously
- 8.2 Bring Philosophy Back to Science
- 8.3 Strive for More Plurality in Science
- 8.4 Reintroduce Mertonian Values
- 8.5 Put Scientists Back to the Ivory Tower
- 8.6 Change the Rules of the Tournament
- 8.7 Protect Scientists from Scientific Journals
- 8.8 Judge Scientists by Their Promises, Not Their Deeds
- 8.9 Teach Honesty as the Guiding Principle of Science
- 8.10 Conclusion
- References
- Chapter 6. Interpretation
- Statistical Glossary
- Index
- No. of pages: 416
- Language: English
- Edition: 1
- Published: June 11, 2015
- Imprint: Academic Press
- Hardback ISBN: 9780124186897
- eBook ISBN: 9780124199569
ÜM
Ülo Maiväli
Affiliations and expertise
University of Tartu, Faculty of Science and Technology, Institute of Technology, Tartu, Estonia