Chang Principles of Scientific Methods

1. Auflage 2014
ISBN: 978-1-4822-3810-5
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 247 Seiten

Principles of Scientific Methods focuses on the fundamental principles behind scientific methods. The book refers to "science" in a broad sense, including natural science, physics, mathematics, statistics, social science, political science, and engineering science. A principle is often abstract and has broad applicability while a method is usually concrete and specific. The author uses many concrete examples to explain principles and presents analogies to connect different methods or problems to arrive at a general principle or a common notion. He mainly discusses a particular method to address the great idea behind the method, not the method itself.

The book shows how the principles are not only applicable to scientific research but also to our daily lives. The author explains how scientific methods are used for understanding how and why things happen, making predictions, and learning how to prevent mistakes and solve problems. Studying the principles of scientific methods is to think about thinking and to enlighten our understanding of scientific research.

Scientific principles are the foundation of scientific methods. In this book, you’ll see how the principles reveal the big ideas behind our scientific discoveries and reflect the fundamental beliefs and wisdoms of scientists. The principles make the scientific methods coherent and constitute the source of creativity.

Zielgruppe

Scientists, researchers, teachers, and undergraduate and graduate students; advanced high school students.

Autoren/Hrsg.

Chang, Mark

Fachgebiete

• Mathematik | Informatik Mathematik Mathematik Allgemein Populäre Darstellungen der Mathematik
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Science in Perspective
Philosophy of Science
Theories of Truth
Determinism and Free Will
The Similarity Principle
The Parsimony Principle
Essence of Understanding
Discovery or Invention
Observation
Experimentation
Interpretation
Qualitative and Quantitative Research

Formal Reasoning
Mathematics as Science
Induction
Deduction
Logic Computation
Mathematical Induction
Thought Experiments
Fitch’s Knowability Paradox
Incompleteness Theorem
Pigeonhole Principle
Proof by Contradiction
Dimensional Analysis

Experimentation
Overview of Experimentation
Experimentation in Life Science
Control and Blinding
Experiment Design
Retrospective and Prospective Studies
Validity and Integrity
Confounding Factors
Variation and Bias
Randomization
Adaptive Experiment
Ethical Issues

Scientific Inference
The Concept of Probability
Probability Distribution
Evidential Measures
Hypothesis Test
Likelihood Principle
Bayesian Reasoning
Causal Space
Decision Theory
Statistical Modeling
Data Mining
Misconceptions and Pitfalls in Statistics

Dynamics of Science
Science as Art
Evolution
Devolution
Classical Game Theory
Evolutionary Game Theory
Networks and Graph Theory
Evolutionary Dynamics of Networks
Brownian Motion
Stochastic Decision Process
Swarm Intelligence
From Ancient Pictograph to Modern Graphics

Controversies and Challenges
Fairness of Social System
Centralized and Decentralized Decisions
Newcomb’s Paradox
The Monty Hall Dilemma
The Two-Envelope Paradox
Simpson’s Paradox
Regression to the Mean
Causation, Association, Correlation, and Confounding
Multiple Testing
Exploratory and Confirmatory Studies
Probability and Statistics Revisited

Case Studies
Social Genius of Animals
Mendel’s Genetics Experiments
Pavlov’s Dogs, Skinner’s Box
Ants That Count!
Disease Outbreak and Network Chaos
Technological Innovation
Critical Path Analysis
Revelations of the Braess Paradox
Artificial Swarm Intelligence
One Stone Three Birds
Scaling in Biology
Genetic Programming
Mechanical Analogy
Numerical Methods
Pyramid and Ponzi Schemes
Material Dating in Archaeology
Molecular Design
Clinical Trials
Publication Bias
Information and Entropy

Bibliography

Index

Über Autor(innen)

Mark Chang is vice president of biometrics at AMAG Pharmaceuticals and an adjunct professor at Boston University. Dr. Chang is an elected fellow of the American Statistical Association and a co-founder of the International Society for Biopharmaceutical Statistics. He serves on the editorial boards of statistical journals and has published seven books in biostatistics and science, including Paradoxes in Scientific Inference, Modern Issues and Methods in Biostatistics, Adaptive Design Theory and Implementation Using SAS and R, and Monte Carlo Simulation for the Pharmaceutical Industry.