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

Includes bibliographical references and index.

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.

Front Cover; Interpreting Biomedical Science: Experiment, Evidence, and Belief; Copyright Page; Contents; Preface; 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; 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?; 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 Practices1.6 Reproducibility of Basic Biomedical Science; 1.6.1 Genome-Wide Association Studies; 1.6.2 Microarray Studies; 1.6.3 Proteomics; 1.6.4 Small 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; 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; 2.8.1 Correlation; 2.8.2 Concordance; 2.8.3 Regression; 2.9 From Correlation to Causation; 2.10 From Experiment to Causation; 2.11 Is Causality a Scientific Concept?; References; II. The Method; 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; 3.6.1 Example 1. Negative Controls; 3.6.2 Example 2. Normalization Controls.

3.6.3 Example 3. Controlling the Controls3.7 Multiplicities; 3.8 Conclusion: How to Design an Experiment; References; 4 Data and Evidence; 4.1 Looking at Data; 4.2 Modeling Data; 4.3 What Is Probability?; 4.3.1 Bayesian Probability; 4.3.2 Frequentist Probability; 4.3.3 Propensity Theory of Probability; 4.4 Assumptions Behind Frequentist Statistical Tests; 4.5 The Null Hypothesis; 4.6 The P value; 4.6.1 What the P Value Is Not; 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 Evidence4.12 Conclusion: Ideologies Behind Some Methods of Statistical Inference; References; 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; III. The Big Picture; 6 Interpretation.