Interpreting biomedical science : experiment, evidence, and belief / Ülo Maiväli.

Format:

Book

Author/Creator:

Maiväli, Ülo, author.

Language:

English

Subjects (All):

Medicine--Research--Methodology.

Medicine.

Medical sciences--Research.

Medical sciences.

Genre:

Electronic books.

Physical Description:

1 online resource

Place of Publication:

London : Academic Press is an imprint of Elsevier, [2015]

System Details:

Mode of access: World Wide Web.

text file

Summary:

Interpreting 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.

Contents:

Part I What Is at Stake: The Skeptical Argument

1 Do We Need a Science of Science?

1.1 Are We Living in the Golden Age of Science? 4

1.2 R&D and the Cost of Medicine 10

1.3 The Efficiency of Drug Discovery 13

1.4 Factors That Endanger the Quality of Medical Evidence 18

1.5 The Stability of Evidence-Based Medical Practices 22

1.6 Reproducibility of Basic Biomedical Science 25

1.6.1 Genome-Wide Association Studies 28

1.6.2 Microarray Studies 32

1.6.3 Proteomics 34

1.6.4 Small Science 36

1.7 Is Reproducibility a Good Criterion of Quality of Research? 39

1.8 Is Biomedical Science Self-Correcting? 42

1.9 Do We Need a Science of Science? 46

References 48

2 The Basis of Knowledge: Causality and Truth

2.1 Scientific Realism and Truth 55

2.2 Hume's Gambit 62

2.3 Kant's Solution 64

2.4 Why Induction Is Poor Deduction 67

2.5 Popper's Solution 69

2.6 Why Deduction Is Poor Induction 75

2.7 Does Lung Cancer Cause Smoking? 81

2.8 Correlation, Concordance, and Regression 85

2.8.1 Correlation 86

2.8.2 Concordance 89

2.8.3 Regression 91

2.9 From Correlation to Causation 94

2.10 From Experiment to Causation 98

2.11 Is Causality a Scientific Concept? 102

References 106

Part II The Method

3 Study Design

3.1 Why Do Experiments? 113

3.2 Population and Sample 119

3.3 Regression to the Mean 124

3.4 Why Repeat an Experiment? 128

3.5 Technical Versus Biological Replication of Experiments 134

3.6 Experimental Controls 137

3.6.1 Example 1. Negative Controls 141

3.6.2 Example 2. Normalization Controls 144

3.6.3 Example 3. Controlling the Controls 146

3.7 Multiplicities 147

3.8 Conclusion: How to Design an Experiment 153

References 154

4 Data and Evidence

4.1 Looking at Data 160

4.2 Modeling Data 166

4.3 What Is Probability? 171

4.3.1 Bayesian Probability 176

4.3.2 Frequentist Probability 177

4.3.3 Propensity Theory of Probability 179

4.4 Assumptions Behind Frequentist Statistical Tests 181

4.5 The Null Hypothesis 183

4.6 The P Value 190

4.6.1 What the P Value Is Not 193

4.7 Neyman-Pearson Hypothesis Testing 194

4.8 Multiple Testing in the Context of NPHT 198

4.9 P Value as a Measure of Evidence 204

4.10 The "Error Bars" 209

4.11 Likelihood as an Unbiased Measure of Evidence 214

4.12 Conclusion: Ideologies Behind Some Methods of Statistical Inference 219

References 220

5 Truth and Belief

5.1 From Long-Run Error Probabilities to Degrees of Belief 224

5.2 Bayes Theorem: What Makes a Rational Being? 226

5.3 Testing in the Infinite Hypothesis Space: Bayesian Parameter Estimation 233

5.4 All Against All: Bayesianism Versus Frequentism Versus Likelihoodism 237

5.5 Bayesianism as a Philosophy 245

5.6 Bayesianism and the Progress of Science 252

5.7 Conclusion to Part II 256

References 258

Part III The Big Picture

6 Interpretation

6.1 Hypothesis Testing at Small Samples 263

6.2 Is Intuitive Reasoning Bayesian? 276

6.3 The Molecular Biology Lab as Research Subject 280

6.4 How to Win Fame and Influence People 282

6.4.1 Seven Commandments for the Unscrupulous 285

References 288

7 Science as a Social Enterprise

7.1 The Revolutionary Road of Thomas Kuhn 292

7.2 The Anarchism of Paul Feyerabend 296

7.3 The Communism of Robert K. Merton 298

7.3.1 The Matthew Effect 304

7.3.2 Artificial Abundance 305

7.3.3 Artificial Scarcity 306

7.3.4 The Winners Curse 307

7.3.5 Herding 308

7.4 Science as an Oligogracy 311

7.4.1 Knowledge Transfer 312

7.4.2 Bibliometrics: Can We Quantify Science? 316

7.5 Tragedy of the Proxy 319

7.6 Science as a Lottery 321

7.7 Science as a Career 328

References 332

8 What Can Be Done: A Utopia

8.1 Take Methodology Seriously 339

8.2 Bring Philosophy Back to Science 341

8.3 Strive for More Plurality in Science 343

8.4 Reintroduce Mertonian Values 345

8.5 Put Scientists Back to the Ivory Tower 348

8.6 Change the Rules of the Tournament 351

8.7 Protect Scientists from Scientific Journals 354

8.8 Judge Scientists by Their Promises, Not Their Deeds 358

8.9 Teach Honesty as the Guiding Principle of Science 360

8.10 Conclusion 361

References 362.

Notes:

Online resource; title from PDF title page (Ebsco, viewed June 19, 2015).

Includes bibliographical references and index.

ISBN:

9780124199569

0124199569

OCLC:

911179264

Access Restriction:

Restricted for use by site license.