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Interpreting biomedical science : experiment, evidence, and belief / / Ülo Maiväli
| Interpreting biomedical science : experiment, evidence, and belief / / Ülo Maiväli |
| Autore | Maiväli Ülo |
| Pubbl/distr/stampa | London, England ; ; San Diego, California : , : Academic Press, an imprint of Elsevier, , 2015 |
| Descrizione fisica | 1 online resource (416 p.) |
| Disciplina | 610.72 |
| Soggetto topico |
Medicine - Research - Methodology
Medical sciences - Research |
| ISBN | 0-12-419956-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 6.1 Hypothesis Testing at Small Samples |
| Record Nr. | UNINA-9910797243303321 |
Maiväli Ülo
|
||
| London, England ; ; San Diego, California : , : Academic Press, an imprint of Elsevier, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Interpreting biomedical science : experiment, evidence, and belief / / Ülo Maiväli
| Interpreting biomedical science : experiment, evidence, and belief / / Ülo Maiväli |
| Autore | Maiväli Ülo |
| Pubbl/distr/stampa | London, England ; ; San Diego, California : , : Academic Press, an imprint of Elsevier, , 2015 |
| Descrizione fisica | 1 online resource (416 p.) |
| Disciplina | 610.72 |
| Soggetto topico |
Medicine - Research - Methodology
Medical sciences - Research |
| ISBN | 0-12-419956-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 6.1 Hypothesis Testing at Small Samples |
| Record Nr. | UNINA-9910822207303321 |
|
Maiväli Ülo
|
||
| London, England ; ; San Diego, California : , : Academic Press, an imprint of Elsevier, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||